Marker‐Assisted Selection and Introgression

Whittaker, JC

doi:10.1002/0470022620.bbc19

Cited by 6 publications

(10 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BACKCROSSING AND GENETIC IMPROVEMENT (a) Optimization of marker-assisted backcrossing In addition to conventional breeding methods, various aspects of the use of molecular markers (for controlling the target genes, accelerating the recovery of recurrent genome or reducing linkage drag) to improve the efficiency of introgression in backcross breeding programmes have been investigated from a theoretical standpoint in recent years. These were reviewed recently Whittaker 2001;Dekkers & Hospital 2002;Hospital 2003) and will not be detailed here. Whether it is called marker-assisted introgression or marker-assisted backcross, use of markers in backcross breeding programmes is efficient.…”

Section: Introductionmentioning

confidence: 99%

Selection in backcross programmes

Hospital

2005

Phil. Trans. R. Soc. B

146

View full text Add to dashboard Cite

Backcrossing is a well-known and long established breeding scheme where a characteristic is introgressed from a donor parent into the genomic background of a recurrent parent. The various uses of backcrossing in modern genetics, particularly with the help of molecular markers, are reviewed here. Selection in backcross programmes is used to either improve the genetic value of plant and animal populations or fine map quantitative trait loci. Both cases are helpful in our understanding of the genetic bases of quantitative traits variation.

show abstract

Section: Introductionmentioning

confidence: 99%

Selection in backcross programmes

Hospital

2005

Phil. Trans. R. Soc. B

146

View full text Add to dashboard Cite

show abstract

“…In conclusion, as illustrated in this study, the skewed t prior distribution proposed offers flexibility and robustness concerning the distribution of genetic effects associated with molecular markers. The use of shrinkage as suggested by Whittaker (2001) and Lange & Whittaker (2001), and generalized into a Bayesian setting by Gianola et al (2003), avoids problems due to overparameterization, and colinearity causing unstable least-squares estimates. The skewed t-distribution retains the desirable properties described in Gianola et al (2003) but also makes it possible to describe differences between lines in an F 2 cross more adequately.…”

Section: Discussionmentioning

confidence: 99%

“…Recent development of molecular techniques has provided a massive number of molecular markers and, as a consequence, dense genetic maps are now available for a number of species. An obvious use of this molecular information is for markerassisted selection in livestock and plant populations (Whittaker, 2001). The basic idea of markerassisted selection is to detect and exploit linkage disequilibrium between mutations that cause genetic variations and presumably neutral molecular markers.…”

Section: Introductionmentioning

confidence: 99%

“…Recently some authors have proposed the combined use of all available markers. For instance, Whittaker (2001) and Lange & Whittaker (2001) proposed ridge regression methods, while Gianola et al (2003) and Xu (2003) developed Bayesian approaches. The procedures of Gianola et al (2003) and Xu (2003) employ prior Gaussian distributions for effects associated with molecular markers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A whole-genome analysis using robust asymmetric distributions

et al. 2006

View full text Add to dashboard Cite

This study is aimed at improving the analysis of data used in identifying marker-associated effects on quantitative traits, specifically to account for possible departures from a Gaussian distribution of the trait data and to allow for asymmetry of marker effects attributable to phenotypic divergence between parental lines. A Bayesian procedure for analysing marker effects at the whole-genome level is presented. The procedure adopts a skewed t-distribution as a prior distribution of marker effects. The model with the skewed t-process includes Gaussian prior distributions, skewed Gaussian prior distributions and symmetric t-distributions as special cases. A Markov Chain Monte Carlo algorithm for obtaining marginal posterior distributions of the unknowns is also presented. The method was applied to a dataset on three traits (live weight, carcass length and backfat depth) measured in an F2 cross between Iberian and Landrace pigs. The distribution of marker effects was clearly asymmetric for carcass length and backfat depth, whereas it was symmetric for live weight. The t-distribution seems more appropriate for describing the distribution of marker effects on backfat depth.

show abstract

“…That is, unless all QTL affecting the traits of interest can be identified, phenotypic values should be combined with the marker scores to increase LMSI efficiency (Dekkers and Settar 2004). Moreau et al (2000) and Whittaker (2003) found that the LMSI is more effective than LPSI only in early generation testing and that LMSI increased costs because of molecular marker evaluation. The LMSI assumes that favorable alleles are known, as are their average effects on phenotype (Lande and Thompson 1990;Hospital et al 1997).…”

Section: Linear Marker Selection Indicesmentioning

confidence: 99%

General Introduction

Cerón-Rojas¹,

Crossa²

2018

Linear Selection Indices in Modern Plant Breeding

View full text Add to dashboard Cite

We describe the main characteristics of two approaches to the linear selection indices theory. The first approach is called standard linear selection indices whereas the second of them is called eigen selection index methods. In the first approach, the economic weights are fixed and known, whereas in the second approach the economic weights are fixed but unknown. This is the main difference between both approaches and implies that the eigen selection index methods include to the standard linear selection indices because they do not require that the economic weights be known. Both types of indices predict the net genetic merit and maximize the selection response, and they give the breeder an objective criterion to select individuals as parents for the next selection cycle. In addition, in the prediction they can use phenotypic, markers, and genomic information. In both approaches, the indices can be unrestricted, null restricted or predetermined proportional gains and can be used in the context of single-stage or multistage breeding selection schemes. We describe the main characteristics of the two approaches to the linear selection indices theory and we finish this chapter describing the Lagrange multiplier method, which is the main tool to maximize the selection index responses.

show abstract

Marker‐Assisted Selection and Introgression

Cited by 6 publications

References 68 publications

Selection in backcross programmes

Selection in backcross programmes

A whole-genome analysis using robust asymmetric distributions

General Introduction

Contact Info

Product

Resources

About