Modeling Quantitative Trait Loci and Interpretation of Models

Zeng, Zhao‐Bang; Wang, Tao; Zou, Wei

doi:10.1534/genetics.104.035857

Cited by 152 publications

(218 citation statements)

References 17 publications

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“…Two main metrics were adopted for the populations derived from the cross between the two inbred lines: the F ∞ and F 2 metrics [33][34][35]. The derivation of the expected genetic values of Z 1i and Z 2i under both the F ∞ and the F 2 metric models is presented in Supplement Information A under the assumption that the quantitative trait was determined by two QTL with digenic epistasis and arbitrary linkage (A and B).…”

Section: Statistical Genetic Models For Mapping Heterotic Qtl In Ril-mentioning

confidence: 99%

Genome-wide mapping of QTL associated with heterosis in the RIL-based NCIII design

Zhang

2012

Chin. Sci. Bull.

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Heterosis represents one of the most revolutionary advancements in crop improvement. In the genetic dissection of heterosis, NCIII design is one of the most powerful and widely used mating schemes. However, the methodologies for quantitative trait loci (QTL) detection in the design were mostly based on composite interval mapping. Therefore, in this study, our purpose was to develop a statistical method for mapping epistatic QTL associated with heterosis in the RIL-based NCIII design. First, we derivated the expectations of two classical linear transformations, Z 1 and Z 2 , while a quantitative trait was controlled by two QTL with digenic epistasis and arbitrary linkage under the F ∞ and F 2 metric models. Then, we constructed an epistatic genetic model that includes all markers on the whole genome simultaneously, and estimated all the parameters in the model by the empirical Bayes approach. Finally, a series of Monte Carlo simulation experiments was carried out to confirm the proposed approach. The results show that: (1) all the augmented genetic parameters for main-effect QTL could be rightly identified with satisfactory statistical power and precision; (2) the statistical powers in the detection of augmented epistatic effects were substantively affected by the signs of pure epistatic effects; (3) it is more difficult to detect epistatic QTL than to detect main-effect QTL; (4) statistical power is higher in the RIL-based NCIII design than in the F 2 -based NCIII design, especially in the detection of the augmented epistatic effect that consists of two pure epistatic effects in opposite directions.empirical Bayes, epistasis, NCIII design, heterosis, quantitative trait locus, recombinant inbred line Citation:He X H, Hu Z L, Zhang Y M. Genome-wide mapping of QTL associated with heterosis in the RIL-based NCIII design.

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Section: Statistical Genetic Models For Mapping Heterotic Qtl In Ril-mentioning

confidence: 99%

Genome-wide mapping of QTL associated with heterosis in the RIL-based NCIII design

Zhang

2012

Chin. Sci. Bull.

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“…With the advances of molecular genetics and genomics, efforts have been made to develop methods and tools for detecting statistical epistasis in different population structures (Malmberg and Mauricio, 2005;Xu and Jia, 2007;Zhang and Liu, 2007;Aylor and Zeng, 2008;Pattin et al, 2008). Research has explored a wide range of topics including statistical modelling (AlvarezCastro and Carlborg, 2007;Moore et al, 2007;Sung and Wijsman, 2007), model parameterization (Zeng et al, 2005;Wang and Zeng, 2006;Alvarez-Castro et al, 2008), search algorithm (Carlborg et al, 2005;Ritchie and Motsinger, 2005;Kooperberg and Leblanc, 2008;Mechanic et al, 2008), multiple testing (Jannink and Jansen, 2001;Sen and Churchill, 2001;Storey et al, 2005;Stich et al, 2007) and computing efficiency (Ljungberg et al, 2004;Bush et al, 2006). A number of relevant software tools have also been made publicly available (Broman et al, 2003;Hahn et al, 2003;Yandell et al, 2007;Yang et al, 2007;Le Rouzic and Alvarez-Castro, 2008).…”

Section: Introductionmentioning

confidence: 99%

Controlling false positives in the mapping of epistatic QTL

et al. 2009

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This study addresses the poorly explored issue of the control of false positive rate (FPR) in the mapping of pair-wise epistatic quantitative trait loci (QTL). A nested test framework was developed to (1) allow pre-identified QTL to be used directly to detect epistasis in one-dimensional genome scans, (2) to detect novel epistatic QTL pairs in twodimensional genome scans and (3) to derive genome-wide thresholds through permutation and handle multiple testing. We used large-scale simulations to evaluate the performance of both the one-and two-dimensional approaches in mapping different forms and levels of epistasis and to generate profiles of FPR, power and accuracy to inform epistasis mapping studies. We showed that the nested test framework and genome-wide thresholds were essential to control FPR at the 5% level. The one-dimensional approach was generally more powerful than the two-dimensional approach in detecting QTL-associated epistasis and identified nearly all epistatic pairs detected from the two-dimensional approach. However, only the two-dimensional approach could detect epistatic QTL with weak main effects. Combining the two approaches allowed effective mapping of different forms of epistasis, whereas using the nested test framework kept the FPR under control. This approach provides a good search engine for high-throughput epistasis analyses.

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“…In quantitative genetics, the joint action of two loci is usually assessed by models assuming linear genetic effects on a given mathematical scale of the quantitative trait (for example, Fisher, 1918;Cockerham, 1954;Cheverud and Routman, 1995;Kao and Zeng, 2002;Zeng et al, 2005). If models do not agree with experimental data, one may say that an interactionepistasis -is present.…”

Section: Introductionmentioning

confidence: 99%

Allelic penetrance approach as a tool to model two-locus interaction in complex binary traits

et al. 2007

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Many binary phenotypes do not follow a classical Mendelian inheritance pattern. Interaction between genetic and environmental factors is thought to contribute to the incomplete penetrance phenomena often observed in these complex binary traits. Several two-locus models for penetrance have been proposed to aid the genetic dissection of binary traits. Such models assume linear genetic effects of both loci in different mathematical scales of penetrance, resembling the analytical framework of quantitative traits. However, changes in phenotypic scale are difficult to envisage in binary traits and limited genetic interpretation is extractable from current modeling of penetrance. To overcome this limitation, we derived an allelic penetrance approach that attributes incomplete penetrance to the stochastic expression of the alleles controlling the phenotype, the genetic background and environmental factors. We applied this approach to formulate dominance and recessiveness in a single diallelic locus and to model different genetic mechanisms for the joint action of two diallelic loci. We fit the models to data on the genetic susceptibility of mice following infections with Listeria monocytogenes and Plasmodium berghei. These models gain in genetic interpretation, because they specify the alleles that are responsible for the genetic (inter)action and their genetic nature (dominant or recessive), and predict genotypic combinations determining the phenotype. Further, we show via computer simulations that the proposed models produce penetrance patterns not captured by traditional two-locus models. This approach provides a new analysis framework for dissecting mechanisms of interlocus joint action in binary traits using genetic crosses.

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Modeling Quantitative Trait Loci and Interpretation of Models

Cited by 152 publications

References 17 publications

Genome-wide mapping of QTL associated with heterosis in the RIL-based NCIII design

Genome-wide mapping of QTL associated with heterosis in the RIL-based NCIII design

Controlling false positives in the mapping of epistatic QTL

Allelic penetrance approach as a tool to model two-locus interaction in complex binary traits

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