scholarly journals Optimal weighting of information in marker-assisted selection

1997 ◽  
Vol 69 (2) ◽  
pp. 137-144 ◽  
Author(s):  
J. C. WHITTAKER ◽  
C. S. HALEY ◽  
R. THOMPSON
Keyword(s):  
Linear Regression ◽  
Marker Assisted Selection ◽  
Quantitative Traits ◽  
Genetic Variance ◽  
Cross Validation ◽  
Inbred Lines ◽  
Response To Selection ◽  
Phenotypic Information ◽  
Optimal Weighting ◽  
Marker Information

In crosses between inbred lines linear regression can be used to estimate marker effects; these marker effects then allow marker-assisted selection (MAS) for quantitative traits. Weighting of marker and phenotypic information in MAS requires estimation of genetic variance associated with the markers: the usual estimators are biased, resulting in too much weight being placed on marker information relative to phenotypic information. In this paper we develop a cross-validation method to remove this bias, and show by simulation that response to selection using this method is almost as high as that achieved using optimal weighting of marker and phenotypic information.

scholarly journals Marker-assisted selection using ridge regression

2000 ◽  
Vol 75 (2) ◽  
pp. 249-252 ◽  
Author(s):  
JOHN C. WHITTAKER ◽  
ROBIN THOMPSON ◽  
MIKE C. DENHAM
Keyword(s):  
Linear Regression ◽  
Ridge Regression ◽  
Marker Assisted Selection ◽  
Quantitative Traits ◽  
Selection Response ◽  
Inbred Lines ◽  
Response To Selection ◽  
Satisfactory Method ◽  
The Mean ◽  
Selection Of

In crosses between inbred lines, linear regression can be used to estimate the correlation of markers with a trait of interest; these marker effects then allow marker assisted selection (MAS) for quantitative traits. Usually a subset of markers to include in the model must be selected: no completely satisfactory method of doing this exists. We show that replacing this selection of markers by ridge regression can improve the mean response to selection and reduce the variability of selection response.

scholarly journals Genomic Prediction and the Practical Breeding of 12 Quantitative-Inherited Traits in Cucumber (Cucumis sativus L.)

2021 ◽  
Vol 12 ◽  
Author(s):  
Ce Liu ◽  
Xiaoxiao Liu ◽  
Yike Han ◽  
Xi'ao Wang ◽  
Yuanyuan Ding ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Complex Traits ◽  
Variance Components ◽  
Genetic Variance ◽  
Cross Validation ◽  
Additive Genetic Variance ◽  
Inbred Lines ◽  
Cucumis Sativus L ◽  
The Cross ◽  
Two Populations

Genomic prediction is an effective way for predicting complex traits, and it is becoming more essential in horticultural crop breeding. In this study, we applied genomic prediction in the breeding of cucumber plants. Eighty-one cucumber inbred lines were genotyped and 16,662 markers were identified to represent the genetic background of cucumber. Two populations, namely, diallel cross population and North Carolina II population, having 268 combinations in total were constructed from 81 inbred lines. Twelve cucumber commercial traits of these two populations in autumn 2018, spring 2019, and spring 2020 were collected for model training. General combining ability (GCA) models under five-fold cross-validation and cross-population validation were applied to model validation. Finally, the GCA performance of 81 inbred lines was estimated. Our results showed that the predictive ability for 12 traits ranged from 0.38 to 0.95 under the cross-validation strategy and ranged from −0.38 to 0.88 under the cross-population strategy. Besides, GCA models containing non-additive effects had significantly better performance than the pure additive GCA model for most of the investigated traits. Furthermore, there were a relatively higher proportion of additive-by-additive genetic variance components estimated by the full GCA model, especially for lower heritability traits, but the proportion of dominant genetic variance components was relatively small and stable. Our findings concluded that a genomic prediction protocol based on the GCA model theoretical framework can be applied to cucumber breeding, and it can also provide a reference for the single-cross breeding system of other crops.

scholarly journals Simulation of marker assisted selection in hybrid populations

1994 ◽  
Vol 63 (1) ◽  
pp. 39-47 ◽  
Author(s):  
A. Gimelfarb ◽  
R. Lande
Keyword(s):  
Molecular Markers ◽  
Linkage Disequilibrium ◽  
Population Size ◽  
Genetic Markers ◽  
Computer Model ◽  
Marker Assisted Selection ◽  
Inbred Lines ◽  
Response To Selection ◽  
Molecular Information ◽  
Efficiency Of Selection

SummaryA computer model is developed that simulates Marker Assisted Selection (MAS) in a population produced by a cross between two inbred lines. Selection is based on an index that incorporates both phenotypic and molecular information. Molecular markers contributing to the index and their relative weights are determined by multiple regression of individual phenotype on the markers. The model is applied to investigate the efficiency of MAS as affected by several factors including total number of markers in the genome, number of markers contributing to the index, population size and heritability of the character. It is demonstrated that selection based on genetic markers can effectively utilize the linkage disequilibrium between genetic markers and QTLs created by crossing inbred lines. Selection is more efficient if markers contributing to the index are re-evaluated each generation than if they are evaluated only once. Increasing the total number of markers in the genome as well as the number of markers contributing to the index does not necessarily result in a higher efficiency of selection. Moreover, too many markers may result in a weaker response to selection. Population size is shown to be the most important factor affecting the efficiency of MAS.

scholarly journals Development of genic KASP SNP markers from RNA-Seq data for map-based cloning and marker-assisted selection in maize

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhengjie Chen ◽  
Dengguo Tang ◽  
Jixing Ni ◽  
Peng Li ◽  
Le Wang ◽  
...  
Keyword(s):  
Marker Assisted Selection ◽  
Inbred Lines ◽  
Average Density ◽  
Snp Markers ◽  
Data Sets ◽  
Rna Seq ◽  
Specific Pcr ◽  
Maize Inbred Lines ◽  
Allele Specific ◽  
Allele Specific Pcr

Abstract Background Maize is one of the most important field crops in the world. Most of the key agronomic traits, including yield traits and plant architecture traits, are quantitative. Fine mapping of genes/ quantitative trait loci (QTL) influencing a key trait is essential for marker-assisted selection (MAS) in maize breeding. However, the SNP markers with high density and high polymorphism are lacking, especially kompetitive allele specific PCR (KASP) SNP markers that can be used for automatic genotyping. To date, a large volume of sequencing data has been produced by the next generation sequencing technology, which provides a good pool of SNP loci for development of SNP markers. In this study, we carried out a multi-step screening method to identify kompetitive allele specific PCR (KASP) SNP markers based on the RNA-Seq data sets of 368 maize inbred lines. Results A total of 2,948,985 SNPs were identified in the high-throughput RNA-Seq data sets with the average density of 1.4 SNP/kb. Of these, 71,311 KASP SNP markers (the average density of 34 KASP SNP/Mb) were developed based on the strict criteria: unique genomic region, bi-allelic, polymorphism information content (PIC) value ≥0.4, and conserved primer sequences, and were mapped on 16,161 genes. These 16,161 genes were annotated to 52 gene ontology (GO) terms, including most of primary and secondary metabolic pathways. Subsequently, the 50 KASP SNP markers with the PIC values ranging from 0.14 to 0.5 in 368 RNA-Seq data sets and with polymorphism between the maize inbred lines 1212 and B73 in in silico analysis were selected to experimentally validate the accuracy and polymorphism of SNPs, resulted in 46 SNPs (92.00%) showed polymorphism between the maize inbred lines 1212 and B73. Moreover, these 46 polymorphic SNPs were utilized to genotype the other 20 maize inbred lines, with all 46 SNPs showing polymorphism in the 20 maize inbred lines, and the PIC value of each SNP was 0.11 to 0.50 with an average of 0.35. The results suggested that the KASP SNP markers developed in this study were accurate and polymorphic. Conclusions These high-density polymorphic KASP SNP markers will be a valuable resource for map-based cloning of QTL/genes and marker-assisted selection in maize. Furthermore, the method used to develop SNP markers in maize can also be applied in other species.

scholarly journals Identification of Associations between SSR Markers and Quantitative Traits of Maize (Zea mays L.)

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 182
Author(s):  
Jan Bocianowski ◽  
Kamila Nowosad ◽  
Barbara Wróbel ◽  
Piotr Szulc
Keyword(s):  
Zea Mays ◽  
Ssr Markers ◽  
Marker Assisted Selection ◽  
Quantitative Traits ◽  
Zea Mays L ◽  
Fresh Weight ◽  
Multiple Traits ◽  
Good Tool ◽  
Maize Varieties ◽  
Hybrid Maize

Microsatellite or simple sequence repeat (SSR) markers have wide applicability for genetic analysis in crop plant improvement strategies. Marker-assisted selection is an important tool for plant breeders to increase the efficiency of a breeding process, especially for multigenic traits, highly influenced by the environment. In this paper, the relationships between SSR markers and 26 quantitative traits of hybrid maize varieties (Zea mays L.) were analyzed. Association analyses were performed based on 30 SSR primers in a set of thirteen hybrid maize varieties. A total of 112 SSR markers were detected in these genotypes. The number of alleles per locus ranged from 1 to 17, with the average number of alleles per locus equal to 3.7. The number of molecular markers associated with observed traits ranged from 1 (for the number of kernels in row, ears weight and fresh weight of one plant) to 14 (for damage of maize caused by P. nubilalis) in 2016 as well as from 1 (for soil plant analysis development—SPAD, the number of grains in ear and fresh weight of one plant) to 12 (for carotenoids content) in 2017. The sum of statistically significant associations between SSR markers and at least one trait was equal to one hundred sixty in 2016 as well as one hundred twenty-five in 2017. Marker trait associations (MTAs) were found on the basis of regression analysis. The proportion of the total phenotypic variances of individual traits explained by the marker ranged from 24.4% to 77.7% in the first year of study and from 24.3% to 77.9% in 2017. Twenty-two SSR markers performed a significant effect on at least one tested trait in both years of experiment. The three markers (phi021/4, phi036/3, and phi061/2) can be a good tool in marker-assisted selection because they allow simultaneous selection for multiple traits in both years of study, such as the number of kernels in row and the number of grains in ear (phi021/4), the number of plant after germination, the number of plants before harvest, and the number of ears (phi036/3), as well as moisture of grain and length of ears (phi061/2).

scholarly journals Components of genetic variability and heritability of grain yield of silage maize

Genetika ◽  
2004 ◽  
Vol 36 (2) ◽  
pp. 121-131 ◽  
Author(s):  
Mile Secanski ◽  
Tomislav Zivanovic ◽  
Goran Todorovic ◽  
Gordana Surlan-Momirovic
Keyword(s):  
Genetic Variability ◽  
Grain Yield ◽  
Genetic Variance ◽  
Block Design ◽  
Inbred Lines ◽  
Narrow Sense Heritability ◽  
Additive Variance ◽  
Silage Maize ◽  
V Genes ◽  
Set Up

The aim of the present study was to evaluate the following parameters for the grain yield of silage maize: variability of inbred lines and their diallel hybrids, superior-parent heterosis and components of genetic variability and heritability on the basis of the diallel set. The two-year four-replicate trial was set up according to the randomized complete-block design at Zemun Polje. It was determined that a genotype, year and their interaction significantly affected variability of this trait. The highest. i.e. the lowest grain yield, on the average for both investigation years. was recorded in the silage maize inbred lines ZPLB402 and ZPLB405. respectively. The analysis of components of genetic variance for grain yield shows that the additive component (D) was lower than the dominant (H1 and H2) genetic variance, while a positive component F and the frequency of dominant (u) and recessive (v) genes for this observed trait point to prevalence of dominant genes over recessive ones. Furthermore. this is confirmed by the ratio of dominant to recessive genes in parental genotypes for grain yield (Kd/Kr> 1) that is greater than unity in both years of investigation. The estimated value of the average degree of dominance (H1/D)1/2 exceeds unity, pointing out to superdominance in inheritance of this trait in both years of investigation. Results of Vr/Vr regression analysis indicate superdominance in inheritance of grain yield. Moreover. a registered presence of non-allelic interaction points out to the need to study effects of epistasis, as it can have a greater significance in certain hybrids. A greater value of dominant than additive variance resulted in high values of broad-sense heritability for grain yield in both investigation years (98.71%, i.e. 97.19% in 1997, i.e. 1998, respectively). and low values of narrow-sense heritability (11.9% in 1997 and 12.2% in 1998).

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