Genomic selection in plant breeding: from theory to practice

Jannink, Jean‐Luc; Lorenz, Aaron J.; Iwata, Hiroyoshi

doi:10.1093/bfgp/elq001

Cited by 987 publications

(807 citation statements)

References 64 publications

Supporting

Mentioning

792

Contrasting

Unclassified

Order By: Relevance

“…For instance, high density SNP arrays have become available in case of chickpea (Roorkiwal et al, 2017), groundnut (Pandey et al, 2017a), and pigeonpea (Saxena et al, 2017 and unpublished). In addition, the efficiency and accuracy with which superior lines can be predicted also depend on the size of the reference population (Jannink et al, 2010;Lorenz et al, 2011). The genetic relatedness or population structure (Saatchi et al, 2011;Riedelsheimer et al, 2013;Wray et al, 2013) may result in overestimating the heritability of the traits (Price et al, 2010;Visscher et al, 2012;Wray et al, 2013).…”

Section: Genotyping Platforms Training Populations and Statistical Mmentioning

confidence: 99%

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

Varshney

Thudi

Pandey

et al. 2018

Journal of Experimental Botany

View full text Add to dashboard Cite

Grain legumes form an important component of the human diet, provide feed for livestock, and replenish soil fertility through biological nitrogen fixation. Globally, the demand for food legumes is increasing as they complement cereals in protein requirements and possess a high percentage of digestible protein. Climate change has enhanced the frequency and intensity of drought stress, posing serious production constraints, especially in rainfed regions where most legumes are produced. Genetic improvement of legumes, like other crops, is mostly based on pedigree and performance-based selection over the past half century. To achieve faster genetic gains in legumes in rainfed conditions, this review proposes the integration of modern genomics approaches, high throughput phenomics, and simulation modelling in support of crop improvement that leads to improved varieties that perform with appropriate agronomy. Selection intensity, generation interval, and improved operational efficiencies in breeding are expected to further enhance the genetic gain in experimental plots. Improved seed access to farmers, combined with appropriate agronomic packages in farmers' fields, will deliver higher genetic gains. Enhanced genetic gains, including not only productivity but also nutritional and market traits, will increase the profitability of farming and the availability of affordable nutritious food especially in developing countries.

show abstract

Section: Genotyping Platforms Training Populations and Statistical Mmentioning

confidence: 99%

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

Varshney

Thudi

Pandey

et al. 2018

Journal of Experimental Botany

View full text Add to dashboard Cite

show abstract

“…Genomic selection (Meuwissen et al, 2001) using a large number of markers has been studied by various researchers in plant populations (Bernardo and Yu, 2007;Piepho, 2009;Jannink et al, 2010;Crossa et al, 2010 among others). Significant literatures also exist in animal breeding research (for example, Gonzalez-Recio et al, 2008;van Raden et al, 2008;de los Campos et al, 2009a;Hayes et al, 2009 andToosi et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Prediction of genetic values of quantitative traits with epistatic effects in plant breeding populations

et al. 2012

View full text Add to dashboard Cite

Though epistasis has long been postulated to have a critical role in genetic regulation of important pathways as well as provide a major source of variation in the process of speciation, the importance of epistasis for genomic selection in the context of plant breeding is still being debated. In this paper, we report the results on the prediction of genetic values with epistatic effects for 280 accessions in the Nebraska Wheat Breeding Program using adaptive mixed least absolute shrinkage and selection operator (LASSO). The development of adaptive mixed LASSO, originally designed for association mapping, for the context of genomic selection is reported. The results show that adaptive mixed LASSO can be successfully applied to the prediction of genetic values while incorporating both marker main effects and epistatic effects. Especially, the prediction accuracy is substantially improved by the inclusion of two-locus epistatic effects (more than onefold in some cases as measured by cross-validation correlation coefficient), which is observed for multiple traits and planting locations. This points to significant potential in using non-additive genetic effects for genomic selection in crop breeding practices.

show abstract

“…1. Consider the typical situation for linear regression, where we have the training set y ∈ R l , x ∈ R l×n , in a standard linear regression, we wish to find parameters β 0 , β such that the sum of square 2 , is minimized. Many machine learning methods have been applied to the genetic trait prediction problem, such as Elastic-Net, Lasso, Ridge Regression [10,11], Bayes A, Bayes B [1], Bayes C π [12], and Bayesian Lasso [13,14].…”

Section: Preliminariesmentioning

confidence: 99%

“…Whole genome prediction of complex phenotypic traits using high-density genotyping arrays has attracted a lot of attention, as it is relevant to the fields of plant and animal breeding and genetic epidemiology [1][2][3][4][5][6][7][8]. Given a set of biallelic molecular markers, such as SNPs, with genotype values typically encoded as {0, 1, 2} on a collection of plant, animal or human samples, the goal is to predict the quantitative trait values by simultaneously modeling all marker effects.…”

Section: Introductionmentioning

confidence: 99%

Does encoding matter? A novel view on the quantitative genetic trait prediction problem

He¹,

Parida²

2015

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

View full text Add to dashboard Cite

Background: Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models which require quantitative encodings for the genotypes. There are lots of work on the prediction algorithms, but none of the existing work investigated the effects of the encodings on the genetic trait prediction problem. Methods: In this work, we view the genetic trait prediction problem from a novel angle: a multiple regression on categorical data problem, which requires encoding the categorical data into numerical data. We further proposed two novel encoding methods and we show that they are able to generate numerical features with higher predictive power. Results and discussion: Our experiments show that our methods are superior to the other encoding methods for both single marker model and epistasis model. We showed that the quantitative genetic trait prediction problem heavily depends on the encoding of genotypes, for both single marker model and epistasis model. Conclusions:We conducted a detailed analysis on the performance of the hybrid encodings. To our knowledge, this is the first work that discusses the effects of encodings for genetic trait prediction problem.

show abstract

Genomic selection in plant breeding: from theory to practice

Cited by 987 publications

References 64 publications

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

Prediction of genetic values of quantitative traits with epistatic effects in plant breeding populations

Does encoding matter? A novel view on the quantitative genetic trait prediction problem

Contact Info

Product

Resources

About