Comparison of methods for regression interval mapping in 

QTL analysis with non-normal traits

Rebaï, Ahmed

doi:10.1017/s0016672396002558

Cited by 77 publications

(72 citation statements)

References 14 publications

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“…We finally used the regression interval mapping method of Haley and Knott (1992) and the nonparametric QTL mapping approach proposed by Kruglyak and Lander (1995), both shown to be robust against the nonnormality of the phenotypes (Rebai 1997). These analyses were implemented using R/QTL (Broman et al 2003).…”

Section: Methodsmentioning

confidence: 99%

Mapping PrBn and Other Quantitative Trait Loci Responsible for the Control of Homeologous Chromosome Pairing in Oilseed Rape (Brassica napus L.) Haploids

et al. 2006

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In allopolyploid species, fair meiosis could be challenged by homeologous chromosome pairing and is usually achieved by the action of homeologous pairing suppressor genes. Oilseed rape (Brassica napus) haploids (AC, n ¼ 19) represent an attractive model for studying the mechanisms used by allopolyploids to ensure the diploid-like meiotic pairing pattern. In oilseed rape haploids, homeologous chromosome pairing at metaphase I was found to be genetically based and controlled by a major gene, PrBn, segregating in a background of polygenic variation. In this study, we have mapped PrBn within a 10-cM interval on the C genome linkage group DY15 and shown that PrBn displays incomplete penetrance or variable expressivity. We have identified three to six minor QTL/BTL that have slight additive effects on the amount of pairing at metaphase I but do not interact with PrBn. We have also detected a number of other loci that interact epistatically, notably with PrBn. Our results support the idea that, as in other polyploid species, metaphase I homeologous pairing in oilseed rape haploids is controlled by an integrated system of several genes, which function in a complex manner.

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Section: Methodsmentioning

confidence: 99%

Mapping PrBn and Other Quantitative Trait Loci Responsible for the Control of Homeologous Chromosome Pairing in Oilseed Rape (Brassica napus L.) Haploids

et al. 2006

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“…QTLs with a LOD score of above 2 were considered significant for further analysis (Churchill and Doerge, 1994;Doerge and Churchill, 1996). Composite interval mapping to assign significance based on the underlying trait distribution is robust at handling normal or near normal trait distributions (Rebai, 1997), as found for most of our phenotypes. The define.peak function implemented in the R/eqtl package was used to identify the peak location and one-LOD interval of each significant QTL for each trait (Wang et al, 2006).…”

Section: Qtl Analysismentioning

confidence: 55%

Quantitative Variation in Responses to Root Spatial Constraint within Arabidopsis thaliana

2015

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ORCID IDs: 0000-0001-9298-629X (B.J.); 0000-0001-5759-3175 (D.J.K.)Among the myriad of environmental stimuli that plants utilize to regulate growth and development to optimize fitness are signals obtained from various sources in the rhizosphere that give an indication of the nutrient status and volume of media available. These signals include chemical signals from other plants, nutrient signals, and thigmotropic interactions that reveal the presence of obstacles to growth. Little is known about the genetics underlying the response of plants to physical constraints present within the rhizosphere. In this study, we show that there is natural variation among Arabidopsis thaliana accessions in their growth response to physical rhizosphere constraints and competition. We mapped growth quantitative trait loci that regulate a positive response of foliar growth to short physical constraints surrounding the root. This is a highly polygenic trait and, using quantitative validation studies, we showed that natural variation in EARLY FLOWERING3 (ELF3) controls the link between root constraint and altered shoot growth. This provides an entry point to study how root and shoot growth are integrated to respond to environmental stimuli.

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“…Every time a new method is developed for discrete traits, the investigator must face challenges from peers about how much improvement can be achieved if the discrete nature of the trait is ignored. These challenges repeatedly occurred and may largely credit (or blame) to the works by Visscher et al (1996) and Rebai (1997) who showed marginal improvement of GLM over LM for binary trait QTL mapping when the binary trait is treated as if it were continuous. Rao and Xu's (1998) conclusion about the ad hoc treatment of categorical trait analysis was slightly different.…”

Section: Discussionmentioning

confidence: 99%

Generalized linear mixed models for mapping multiple quantitative trait loci

Che

2012

Heredity

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Many biological traits are discretely distributed in phenotype but continuously distributed in genetics because they are controlled by multiple genes and environmental variants. Due to the quantitative nature of the genetic background, these multiple genes are called quantitative trait loci (QTL). When the QTL effects are treated as random, they can be estimated in a single generalized linear mixed model (GLMM), even if the number of QTL may be larger than the sample size. The GLMM in its original form cannot be applied to QTL mapping for discrete traits if there are missing genotypes. We examined two alternative missing genotype-handling methods: the expectation method and the overdispersion method. Simulation studies show that the two methods are efficient for multiple QTL mapping (MQM) under the GLMM framework. The overdispersion method showed slight advantages over the expectation method in terms of smaller mean-squared errors of the estimated QTL effects. The two methods of GLMM were applied to MQM for the female fertility trait of wheat. Multiple QTL were detected to control the variation of the number of seeded spikelets. (Henderson, 1950). This technique has been used to map genes controlling the variation of quantitative traits (Xu and Yi, 2000;Boer et al, 2007). The LMM methodology cannot be directly applied to traits with discrete distributions. Wedderburn (1974) proposed a linear predictor and a link function to handle discrete traits. The linear predictor is simply a linear model combining information from the independent variables. The link function is used to describe the relationship between the linear predictor and the expectation of the response variable. This approach eventually leads to a special area of statistics called the generalized linear model (GLM) (McCullagh and Nelder, 1989).Xu and Hu (2010) recently developed a GLM approach to interval mapping (IM) for traits with discrete distribution. The purpose of that study was to investigate the efficiencies of two different methods for handling missing genotypes: (1) the heterogeneous residual variance method and (2) the mixture model method. In the first method (heterogeneous residual variance method), we replaced the missing genotypes of quantitative trait loci (QTL) by the conditional expectations of the genotype indicator variables and then took into account the heterogeneous residual variances of different genotypes due to heterogeneous information contents. In the second method (the mixture model method), we fully utilized the conditional distributions of the missing genotypes. Theoretically, the mixture model approach should be optimal. In practice, the heterogeneous residual variance method is more efficient because it is robust to departure from the assumed normal distribution of the residuals. On the contrary, the

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Comparison of methods for regression interval mapping in QTL analysis with non-normal traits

Cited by 77 publications

References 14 publications

Mapping PrBn and Other Quantitative Trait Loci Responsible for the Control of Homeologous Chromosome Pairing in Oilseed Rape (Brassica napus L.) Haploids

Mapping PrBn and Other Quantitative Trait Loci Responsible for the Control of Homeologous Chromosome Pairing in Oilseed Rape (Brassica napus L.) Haploids

Quantitative Variation in Responses to Root Spatial Constraint within Arabidopsis thaliana

Generalized linear mixed models for mapping multiple quantitative trait loci

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