Trait-based analyses for the detection of linkage between marker loci and quantitative trait loci in crosses between inbred lines

Lebowitz, R. J.; Soller, Moshe; Beckmann, Jacques S.

doi:10.1007/bf00289194

Cited by 175 publications

(106 citation statements)

References 15 publications

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“…This is expected because for a normally distributed phenotype, it has been shown that, for a fixed number of genotyped individuals, SG is more powerful than Full [3,14,15], but when genotyping is not constrained, Full is more powerful than SG. This is because the SG scenario excludes some of the information that is used in the Full scenario, as does the SGI scenario.…”

Section: Power Differences Between Genotyping Scenariosmentioning

confidence: 99%

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Comparison of methods for analysis of selective genotyping survival data

et al. 2006

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-Survival traits and selective genotyping datasets are typically not normally distributed, thus common models used to identify QTL may not be statistically appropriate for their analysis. The objective of the present study was to compare models for identification of QTL associated with survival traits, in particular when combined with selective genotyping. Data were simulated to model the survival distribution of a population of chickens challenged with Marek disease virus. Cox proportional hazards (CPH), linear regression (LR), and Weibull models were compared for their appropriateness to analyze the data, ability to identify associations of marker alleles with survival, and estimation of effects when all individuals were genotyped (full genotyping) and when selective genotyping was used. Little difference in power was found between the CPH and the LR model for low censoring cases for both full and selective genotyping. The simulated data were not transformed to follow a Weibull distribution and, as a result, the Weibull model generally resulted in less power than the other two models and overestimated effects. Effect estimates from LR and CPH were unbiased when all individuals were genotyped, but overestimated when selective genotyping was used. Thus, LR is preferred for analyzing survival data when the amount of censoring is low because of ease of implementation and interpretation. Including phenotypic data of non-genotyped individuals in selective genotyping analysis increased power, but resulted in LR having an inflated false positive rate, and therefore the CPH model is preferred for this scenario, although transformation of the data may also make the Weibull model appropriate for this case. The results from the research presented herein are directly applicable to interval mapping analyses.survival / Cox proportional hazards / Weibull / quantitative trait loci

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Section: Power Differences Between Genotyping Scenariosmentioning

confidence: 99%

“…Selective genotyping is a method to reduce the costs of an experiment by genotyping only individuals from the extremes of the phenotypic distribution [14,15]. Individuals from the phenotypic extremes provide most of the power for a marker-trait analysis, so power can be maximized for a limited number of genotypes by selective genotyping.…”

Section: Introductionmentioning

confidence: 99%

Comparison of methods for analysis of selective genotyping survival data

et al. 2006

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“…Selective genotyping (Lander & Botstein, 1989 ;Darvasi & Soller, 1992) reduces time and costs by only analysing cross progeny with extreme phenotype, as these individuals provide the most linkage information. When analysing only the extreme progeny, one can use changes in marker allele frequency in the selected group to infer linkage (Lebowitz et al, 1987). Markers that are linked to alleles that influence the trait should change in frequency in the selected group, whereas the frequency of unlinked markers should remain unchanged.…”

Section: Introductionmentioning

confidence: 99%

“…Previous models (Lebowitz et al, 1987 ;Kim & Stephan, 1999) that have dealt with changes in marker allele frequency in gene mapping experiments, have focused on artificial selection experiments in sexual progeny and examined changes in marker frequency as a result of several generations of sexual reproduction and selection. In this paper, however, we provide the basic theoretical framework for the strategy of picking out the extreme individuals in pooled asexual progeny by selecting for the trait over many generations.…”

Section: Introductionmentioning

confidence: 99%

Mapping Mendelian traits in asexual progeny using changes in marker allele frequency

Logeswaran

Barton

2011

Genet. Res.

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SummaryLinkage between markers and genes that affect a phenotype of interest may be determined by examining differences in marker allele frequency in the extreme progeny of a cross between two inbred lines. This strategy is usually employed when pooling is used to reduce genotyping costs. When the cross progeny are asexual, the extreme progeny may be selected by multiple generations of asexual reproduction and selection. We analyse this method of measuring phenotype in asexual progeny and examine the changes in marker allele frequency due to selection over many generations. Stochasticity in marker frequency in the selected population arises due to the finite initial population size. We derive the distribution of marker frequency as a result of selection at a single major locus, and show that in order to avoid spurious changes in marker allele frequency in the selected population, the initial population size should be in the low to mid hundreds.

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“…Following Lebowitz et al, (1987), we will refer to the ''dichotomizing'' approach for mapping quantitative trait loci (QTL) using LD at the population level as the trait-based (TB) approach, and to the ''non-dichotomizing'' approach as the marker-based (MB) approach. TB methods dichotomize individuals into two classes and, therefore, information contained within each class is lost.…”

Section: Introductionmentioning

confidence: 99%

Mapping Quantitative Trait Loci Using Linkage Disequilibrium: Marker- versus Trait-based Methods

et al. 2005

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Two approaches for mapping quantitative trait loci (QTL) using linkage disequilibrium at the population level were investigated. In the trait-based (TB) approach, the frequencies of marker alleles (or genotypes) are compared in individuals selected from the two tails of the trait distribution. The TB approach uses phenotypic information only in the selection step. In the marker-based (MB) approach, the quantitative trait values for the marker genotypes in the selected individuals are compared. The MB approach uses both the difference in marker allele (or genotype) frequencies and the phenotypic values of each marker genotype in the selected samples. We quantify the power of each approach and show that the power of the MB approach is greater than or equal to that of the TB approach. The advantage of the former is expected to increase with increasing number of traits phenotyped. Our accurate predictions obviate the need for elaborate simulation studies.

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Trait-based analyses for the detection of linkage between marker loci and quantitative trait loci in crosses between inbred lines

Cited by 175 publications

References 15 publications

Comparison of methods for analysis of selective genotyping survival data

Comparison of methods for analysis of selective genotyping survival data

Mapping Mendelian traits in asexual progeny using changes in marker allele frequency

Mapping Quantitative Trait Loci Using Linkage Disequilibrium: Marker- versus Trait-based Methods

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