Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast.

Experiments in model organisms report abundant genetic interactions underlying biologically important traits, whereas quantitative genetics theory predicts, and data support, the notion that most genetic variance in populations is additive. Here we describe networks of capacitating genetic interactions that contribute to quantitative trait variation in a large yeast intercross population. The additive variance explained by individual loci in a network is highly dependent on the allele frequencies of the interacting loci. Modeling of phenotypes for multilocus genotype classes in the epistatic networks is often improved by accounting for the interactions. We discuss the implications of these results for attempts to dissect genetic architectures and to predict individual phenotypes and long-term responses to selection

Consequences of epistasis on growth in an erhualian × white duroc pig cross

Epistasis describes an interaction between the effects of loci. We included epistasis in quantitative trait locus (QTL) mapping of growth at a series of ages in a cross of a Chinese pig breed, Erhualian, with a commercial line, White Duroc. Erhualian pigs have much lower growth rates than White Duroc. We improved a method for genomewide testing of epistasis and present a clear analysis workflow. We also suggest a new approach for interpreting epistasis results where significant additive and dominance effects of a locus in specific backgrounds are determined. In total, seventeen QTL were found and eleven showed epistasis. Loci on chromosomes 2, 3, 4 and 7 were highlighted as affecting growth at more than one age or forming an interaction network. Epistasis resulted in both the QTL on chromosomes 3 and 7 having effects in opposite directions. We believe it is the first time for the chromosome 7 locus that an allele from a Chinese breed has been found to decrease growth. The consequences of epistasis were diverse. Results were impacted by using growth rather than body weight as the phenotype and by correcting for an effect of mother. Epistasis made a considerable contribution to growth in this population and modelling epistasis was important for accurately determining QTL effects

Quantitative Genetics and Functional-Structural Plant Growth Models: Simulation of Quantitative Trait Loci Detection for Model Parameters and Application to Potential Yield Optimization

Background and Aims: Prediction of phenotypic traits from new genotypes under untested environmental conditions is crucial to build simulations of breeding strategies to improve target traits. Although the plant response to environmental stresses is characterized by both architectural and functional plasticity, recent attempts to integrate biological knowledge into genetics models have mainly concerned specific physiological processes or crop models without architecture, and thus may prove limited when studying genotype x environment interactions. Consequently, this paper presents a simulation study introducing genetics into a functional-structural growth model, which gives access to more fundamental traits for quantitative trait loci (QTL) detection and thus to promising tools for yield optimization. Methods: The GreenLab model was selected as a reasonable choice to link growth model parameters to QTL. Virtual genes and virtual chromosomes were defined to build a simple genetic model that drove the settings of the species-specific parameters of the model. The QTL Cartographer software was used to study QTL detection of simulated plant traits. A genetic algorithm was implemented to define the ideotype for yield maximization based on the model parameters and the associated allelic combination. Key Results and Conclusions: By keeping the environmental factors constant and using a virtual population with a large number of individuals generated by a Mendelian genetic model, results for an ideal case could be simulated. Virtual QTL detection was compared in the case of phenotypic traits - such as cob weight - and when traits were model parameters, and was found to be more accurate in the latter case. The practical interest of this approach is illustrated by calculating the parameters (and the corresponding genotype) associated with yield optimization of a GreenLab maize model. The paper discusses the potentials of GreenLab to represent environment x genotype interactions, in particular through its main state variable, the ratio of biomass supply over demand

QTLRel: an R Package for Genome-wide Association Studies in which Relatedness is a Concern

BACKGROUND Existing software for quantitative trait mapping is either not able to model polygenic variation or does not allow incorporation of more than one genetic variance component. Improperly modeling the genetic relatedness among subjects can result in excessive false positives. We have developed an R package, QTLRel, to enable more flexible modeling of genetic relatedness as well as covariates and non-genetic variance components. RESULTS We have successfully used the package to analyze many datasets, including F₃₄ body weight data that contains 688 individuals genotyped at 3105 SNP markers and identified 11 QTL. It took 295 seconds to estimate variance components and 70 seconds to perform the genome scan on an Linux machine equipped with a 2.40GHz Intel(R) Core(TM)2 Quad CPU. CONCLUSIONS QTLRel provides a toolkit for genome-wide association studies that is capable of calculating genetic incidence matrices from pedigrees, estimating variance components, performing genome scans, incorporating interactive covariates and genetic and non-genetic variance components, as well as other functionalities such as multiple-QTL mapping and genome-wide epistasis.This project was supported by NIH grants R01DA021336, R01MH079103 and R21DA024845

Single QTL effects, epistasis, and pleiotropy account for two-thirds of the phenotypic F(2) variance of growth and obesity in DU6i x DBA/2 mice

Genes influencing body weight and composition and serum concentrations of leptin, insulin, and insulin-like growth factor I (IGF-I) in nonfasting animals were mapped in an intercross of the extreme high-growth mouse line DU6i and the inbred line DBA/2. Significant loci with major effects (F > 7.07) for body weight, obesity, and muscle weight were found on chromosomes 1, 4, 5, 7, 11, 12, 13, and 17, for leptin on chromosome 14, for insulin on chromosome 4, and for IGF-I on chromosome 10 at the Igf1 gene locus itself and on chromosome 18. Significant interaction between different quantitative trait loci (QTL) positions was observed (P < 0.01). Evidence was found that loci having small direct effect on growth or obesity contribute to the obese phenotype by gene–gene interaction. The effects of QTLs, epistasis, and pleiotropy account for 64% and 63% of the phenotypic variance of body weight and fat accumulation and for over 32% of muscle weight and serum concentrations of leptin, and IGF-I in the F2 population of DU6i x DBA/2 mice. [The quantitative trait loci described in this paper have been submitted to the Mouse Genome Database.

QTL Study to Reveal Soybean Response on Abiotic and Biotic Stresses

As an important grain legume, the improved soybean(Glycine max [L.] Merr.) adaptive to environmental changesis a valuable genetic resource. Strategy to minimize theimpact of climate effects should be underlined on soybeanproduction encompassing advanced genomics and wellpredicted future climate. Crops including soybean respondto climate change in the aspect of abiotic and bioticenvironmental factors. To predict soybean response toabiotic and biotic stresses, current progress of quantitativetrait loci (QTL) for abiotic and biotic stresses and floweringand related genomic resources could be accessed atSoyBase (http://www.soybase.org) and Phytozome(http://www.phytozome.net). As the involvement of abioticand biotic stresses modulating flowering in soybean, geneslinked to QTL for abiotic/biotic stress and flowering/maturitywere also potential for resisting the environmental changes.By mapping QTLs for flowering using one population indifferent locations (Korea and China) with distinctivelongitude, latitude, and altitude, syntenic correlationbetween these two QTLs on soybean chromosomes 6 and13 indicates the environmental specific role of syntenicregions. The information on QTL and related candidategenes may assist marker-assisted breeding and enactsoybean as a model of adaptive legume crop under abiotic/biotic stress

A bi-dimensional genome scan for prolificacy traits in pigs shows the existence of multiple epistatic QTL

Background: Prolificacy is the most important trait influencing the reproductive efficiency of pig production systems. The low heritability and sex-limited expression of prolificacy have hindered to some extent the improvement of this trait through artificial selection. Moreover, the relative contributions of additive, dominant and epistatic QTL to the genetic variance of pig prolificacy remain to be defined. In this work, we have undertaken this issue by performing one-dimensional and bi-dimensional genome scans for number of piglets born alive (NBA) and total number of piglets born (TNB) in a three generation Iberian by Meishan F2 intercross. Results: The one-dimensional genome scan for NBA and TNB revealed the existence of two genome-wide highly significant QTL located on SSC13 (P < 0.001) and SSC17 (P < 0.01) with effects on both traits. This relative paucity of significant results contrasted very strongly with the wide array of highly significant epistatic QTL that emerged in the bi-dimensional genome-wide scan analysis. As much as 18 epistatic QTL were found for NBA (four at P < 0.01 and five at P < 0.05) and TNB (three at P < 0.01 and six at P < 0.05), respectively. These epistatic QTL were distributed in multiple genomic regions, which covered 13 of the 18 pig autosomes, and they had small individual effects that ranged between 3 to 4% of the phenotypic variance. Different patterns of interactions (a × a, a × d, d × a and d × d) were found amongst the epistatic QTL pairs identified in the current work. Conclusions: The complex inheritance of prolificacy traits in pigs has been evidenced by identifying multiple additive (SSC13 and SSC17), dominant and epistatic QTL in an Iberian × Meishan F2 intercross. Our results demonstrate that a significant fraction of the phenotypic variance of swine prolificacy traits can be attributed to first-order gene-by-gene interactions emphasizing that the phenotypic effects of alleles might be strongly modulated by the genetic background where they segregate