Mapping viability loci using molecular markers

Luo, Lijun; Xu, Shizhong

doi:10.1038/sj.hdy.6800264

Cited by 64 publications

(41 citation statements)

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“…These traits are vulnerable to environments and the criterion of sterility level is not consistent among different researchers. Some methods have been developed to map sterile genes or segregation distortion loci (Vogl and Xu, 2000;Luo and Xu, 2003;Luo et al, 2004;Wang et al, 2005;Chen and Walsh, 2009). These approaches take advantage of using the distorted segregating molecular markers linked to sterile genes, which circumvents the problems with seed set, rate of aborted pollen or embryo-sac morphology, but none of them took into account the existence of epistasis among sterile loci.…”

Section: Discussionmentioning

confidence: 99%

“…The ML method, the bayesian method and the multiresponse nonlinear regression method based on molecular markers were developed to map segregation-distorting loci (SDL) that caused deviations from expected Mendelian segregation ratios in different crops (Vogl and Xu, 2000;Harushima et al, 2001;Harushima et al, 2002;Lu et al, 2002). Luo and Xu (2003) extended the ML method to map viability loci in outbred populations, which used the observed marker genotypes as data and the proportions of the genotypes of the viability locus as parameters. In two F 2 populations of rice (Oryza sativa L.), a multipoint method of ML was used to estimate the positions and effects of the SDLs (Wang et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Genetic mapping of sterile genes with epistasis in backcross designs

2013

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The mapping of sterile genes is an essential issue, which should be solved for the investigation of sterility mechanism in wide hybridization of plants. However, the methods formerly developed cannot address the problem of mapping sterile loci with epistasis. In this study, we developed a new method to map sterile genes with epistasis in wide hybridizations of plants using a backcross design. The maximum likelihood method was used to estimate the parameters of recombination fractions and effects of sterile genes, and the convergent results of these parameters were obtained using the expectation maximization (EM) algorithm. The application and efficiency of this method were tested and demonstrated by a set of simulated data and real data analysis. Results from the simulation experiments showed that the method works well for simultaneously estimating the positions and effects of sterile genes, as well as the epistasis between sterile genes. A real data set of a backcross (BC) population from an interspecific hybrid between cultivated rice, Oryza sativa, and its wild African relative, Oryza longistaminata, was analyzed using the new method. Five sterile genes were detected on the chromosomes of 1, 3, 6, 8 and 10, and significant epistatic effects were found among the four pairs of sterile genes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genetic mapping of sterile genes with epistasis in backcross designs

2013

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“…Our treatment differs from the works carried out by others (Hedrick and Muona, 1990;Fu and Ritland, 1994a, b;Mitchell-Olds, 1995) in that they only tried to estimate and test either the selection coefficient (s) or the degree of dominance (h) but not both. Work previously carried out in our lab (Vogl and Xu, 2000;Luo and Xu, 2003) dealt with either a backcross (BC) in which the degree of dominance was irrelevant or a fourway cross in which the degree of dominance was formulated differently from that in the biallelic system.…”

Section: Theory and Methodsmentioning

confidence: 99%

“…More recently, Vogl and Xu (2000) took a Bayesian approach to mapping multiple viability loci using a backcross mating design as an example. Luo and Xu (2003) extended the ML method of Mitchell-Olds (1995) to estimate the degree of dominance and viability selection at the allelic level (a kind of additive effect). The type of population examined by Luo and Xu (2003) was a four-way cross family, which mimics an outbred full-sib family.…”

Section: Introductionmentioning

confidence: 99%

A quantitative genetics model for viability selection

Luo

Zhang

2004

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Viability selection will change gene frequencies of loci controlling fitness. Consequently, the frequencies of marker loci linked to the viability loci will also change. In genetic mapping, the change of marker allelic frequencies is reflected by the departure from Mendelian segregation ratio. The non-Mendelian segregation of markers has been used to map viability loci along the genome. However, current methods have not been able to detect the amount of selection (s) and the degree of dominance (h) simultaneously. We developed a method to detect both s and h using an F2 mating design under the classical fitness model.We also developed a quantitative genetics model for viability selection by proposing a continuous liability controlling the viability of individuals. With the liability model, mapping viability loci has been formulated as mapping quantitative trait loci. As a result, nongenetic systematic environmental effects can be easily incorporated into the model and subsequently separated from the genetic effects of the viability loci. The quantitative genetic model has been verified with a series of Monte Carlo simulation experiments.

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“…These methods are quite different from the usual QTL mapping procedures. Luo and Xu (2003) first developed an EM (expectation and maximization) algorithm for mapping viability selection loci (the same as SDLs). Luo et al (2005) further developed a quantitative genetic model to map these loci.…”

Section: Introductionmentioning

confidence: 99%

Mapping quantitative trait loci in selected breeding populations: A segregation distortion approach

Cui

Zhang

et al. 2015

Heredity

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Quantitative trait locus (QTL) mapping is often conducted in line-crossing experiments where a sample of individuals is randomly selected from a pool of all potential progeny. QTLs detected from such an experiment are important for us to understand the genetic mechanisms governing a complex trait, but may not be directly relevant to plant breeding if they are not detected from the breeding population where selection is targeting for. QTLs segregating in one population may not necessarily segregate in another population. To facilitate marker-assisted selection, QTLs must be detected from the very population which the selection is targeting. However, selected breeding populations often have depleted genetic variation with small population sizes, resulting in low power in detecting useful QTLs. On the other hand, if selection is effective, loci controlling the selected trait will deviate from the expected Mendelian segregation ratio. In this study, we proposed to detect QTLs in selected breeding populations via the detection of marker segregation distortion in either a single population or multiple populations using the same selection scheme. Simulation studies showed that QTL can be detected in strong selected populations with selected population sizes as small as 25 plants. We applied the new method to detect QTLs in two breeding populations of rice selected for high grain yield. Seven QTLs were identified, four of which have been validated in advanced generations in a follow-up study. Cloned genes in the vicinity of the four QTLs were also reported in the literatures. This mapping-by-selection approach provides a new avenue for breeders to improve breeding progress. The new method can be applied to breeding programs not only in rice but also in other agricultural species including crops, trees and animals. Heredity (2015) 115, 538-546; doi:10.1038/hdy.2015.56; published online 1 July 2015 INTRODUCTIONOver a century of breeding efforts has produced numerous varieties of domestic plants and animals to provide ample food resources for human. The great successes in plant and animal breeding have largely been achieved by exploiting within-species genetic variation for traits of interest through phenotypic selection. Although appropriate phenotypic selection is effective to exploit useful genetic variation of complex traits in breeding populations, the rich sources of naturally occurring genetic variation in plants and animals are largely hidden at the phenotypic levels and remain uncharacterized at the genomic and molecular levels. As a result, they are very much under-utilized in the past breeding programs. Meanwhile, the past decades have witnessed tremendous progress in genetic dissections of complex traits in plants and animals using DNA markers and genomic technologies (Francia et al., 2005;Collard and Mackill, 2008;Miah et al., 2013). During this period of time, thousands of quantitative trait locus (QTL) affecting a wide range of complex traits have been identified in different plant and animal species. These ...

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Mapping viability loci using molecular markers

Cited by 64 publications

References 28 publications

Genetic mapping of sterile genes with epistasis in backcross designs

Genetic mapping of sterile genes with epistasis in backcross designs

A quantitative genetics model for viability selection

Mapping quantitative trait loci in selected breeding populations: A segregation distortion approach

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