Quantitative Trait Loci Mapping Experiments Research Articles

Intraspecific diversity in the mechanisms underlying abamectin resistance in a cosmopolitan pest.

Pesticide resistance relies on a myriad of mechanisms, ranging from single mutations to a complex and polygenic architecture, and it involves mechanisms such as target-site insensitivity, metabolic detoxification, or a combination of these, with either additive or synergistic effects. Several resistance mechanisms against abamectin, a macrocyclic lactone widely used in crop protection, have been reported in the cosmopolitan pest Tetranychus urticae. However, it has been shown that a single mechanism cannot account for the high levels of abamectin resistance found across different mite populations. Here, we used experimental evolution combined with bulked segregant analyses to map quantitative trait loci (QTL) associated with abamectin resistance in two genetically unrelated populations of T. urticae. In these two independent QTL mapping experiments, three and four QTLs were identified, of which three were shared between experiments. Shared QTLs contained genes encoding subunits of the glutamate-gated chloride channel (GluCl) and harboured previously reported mutations, including G314D in GluCl1 and G326E in GluCl3, but also novel resistance candidate loci, including DNA helicases and chemosensory receptors. Surprisingly, the fourth QTL, present only in only one of the experiments and thus unique for one resistant parental line, revealed a non-functional variant of GluCl2, suggesting gene knock-out as resistance mechanism. Our study uncovers the complex basis of abamectin resistance, and it highlights the intraspecific diversity of genetic mechanisms underlying resistance in a cosmopolitan pest.

Identification of Non-Pleiotropic Loci in Flowering and Maturity Control in Soybean

Pleiotropy is considered to have a significant impact on multi-trait evolution, but its roles in the evolution of domestication-related traits in crop species have been unclear. In soybean, several known quantitative trait loci (QTL) controlling maturity, called the maturity loci, are known to have major effects on both flowering and maturity in a highly correlated pleiotropic manner. Aiming at the identification of non-pleiotropic QTLs that independently control flowering and maturity and dissecting the effects of pleiotropy in these important agronomic traits, we conducted a QTL mapping experiment by creating a population from a cross between domesticated soybean G. max and its wild ancestor G. soja that underwent stringent selection for non-pleiotropy in flowering and maturity. Our QTL mapping analyses using the experimental population revealed novel loci that acted in a non-pleiotropic manner: R1-1 controlled primarily flowering and R8-1 and R8-2 controlled maturity, while R1-1 overlapped with QTL, affecting other agronomic traits. Our results suggest that pleiotropy in flowering and maturity can be genetically separated, while artificial selection during soybean domestication and diversification may have favored pleiotropic loci such as E loci that control both flowering and maturity. The non-pleiotropic loci identified in this study will help to identify valuable novel genes to optimize soybean’s life history traits and to improve soybean’s yield potential under diverse environments and cultivation schemes.

Designing a QTL Mapping Study for Implementation in the Realized Collaborative Cross Genetic Reference Population.

The Collaborative Cross (CC) mouse resource is a next-generation mouse genetic reference population (GRP) designed for high-resolution mapping of quantitative trait loci (QTL) of large effect affecting complex traits during health and disease. The CC resource consists of a set of 72 recombinant inbred lines (RILs) generated by reciprocal crossing of five classical and three wild-derived mouse founder strains. Complex traits are controlled by variations within multiple genes and environmental factors, and their mutual interactions. These traits are observed at multiple levels of the animals' systems, including metabolism, body weight, immune profile, and susceptibility or resistance to the development and progress of infectious or chronic diseases. Herein, we present general guidelines for design of QTL mapping experiments using the CC resource-along with full step-by-step protocols and methods that were implemented in our lab for the phenotypic and genotypic characterization of the different CC lines-for mapping the genes underlying host response to infectious and chronic diseases. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: CC lines for whole body mass index (BMI) Basic Protocol 2: A detailed assessment of the power to detect effect sizes based on the number of lines used, and the number of replicates per line Basic Protocol 3: Obtaining power for QTL with given target effect by interpolating in Table 1 of Keele etal. (2019).

Power and precision of QTL mapping in simulated multiple porcine F2 crosses using whole-genome sequence information

BackgroundDuring the last two decades, many QTL (quantitative trait locus) mapping experiments in pigs have been conducted using F2 crosses established from two outbred founder breeds. The founder breeds were frequently chosen from the Asian and European type breeds. A combination of next-generation sequencing, SNP (single nucleotide polymorphism) genotyping technology using SNP-chips, and genotype imputation techniques, can be used to infer the sequence information of all F2 individuals in a cost-effective way. The aim of the present simulation study was to analyze the power and precision of genome-wide association studies (GWASs) with whole-genome sequence data in several types of F2 crosses, including pooled crosses.MethodsBased on a common historical population, three breeds representing two European type breeds (EU1 and EU2) and one Asian type breed (AS) were simulated. Two F2 designs of 500 individuals each were simulated. The cross EU1xEU2 (ASxEU2) was simulated using the phylogenetically closely related breeds EU1 and EU2 (or distantly related breeds AS and EU2) as the founder breeds. The simulated genomes comprised ten chromosomes, each with a length of 1 Morgan and whole-genome sequence information. A polygenic trait with a heritability of 0.5, which was affected by approximately 20 QTL per Morgan, was simulated. GWASs were conducted using single marker mixed linear models, either within the crosses or in their pooled datasets. Additionally, the studies were conducted in the breed EU2, which was a founder breed in both simulated crosses.ResultsThe power to map QTL was high (low) in the ASxEU2 (EU1xEU2) cross and was highest when the data of both crosses were analyzed jointly. By contrast, the mapping precision was the highest in the EU1xEU2 cross. Pooling data led to a precision that was in between the precision of the EU1xEU2 cross and the ASxEU2 cross. A higher mapping precision was observed for QTL segregating within a founder breed.ConclusionsThese results suggest that the existing F2 crosses are promising databases for QTL mapping when the founder breeds are closely related or several crosses can be pooled. This conclusion is particularly applicable for QTL that segregate in a founder breed.

High-density molecular characterization and association mapping in Ethiopian durum wheat landraces reveals high diversity and potential for wheat breeding.

SummaryDurum wheat (Triticum turgidum subsp. durum) is a key crop worldwide, and yet, its improvement and adaptation to emerging environmental threats is made difficult by the limited amount of allelic variation included in its elite pool. New allelic diversity may provide novel loci to international crop breeding through quantitative trait loci (QTL) mapping in unexplored material. Here, we report the extensive molecular and phenotypic characterization of hundreds of Ethiopian durum wheat landraces and several Ethiopian improved lines. We test 81 587 markers scoring 30 155 single nucleotide polymorphisms and use them to survey the diversity, structure, and genome‐specific variation in the panel. We show the uniqueness of Ethiopian germplasm using a siding collection of Mediterranean durum wheat accessions. We phenotype the Ethiopian panel for ten agronomic traits in two highly diversified Ethiopian environments for two consecutive years and use this information to conduct a genome‐wide association study. We identify several loci underpinning agronomic traits of interest, both confirming loci already reported and describing new promising genomic regions. These loci may be efficiently targeted with molecular markers already available to conduct marker‐assisted selection in Ethiopian and international wheat. We show that Ethiopian durum wheat represents an important and mostly unexplored source of durum wheat diversity. The panel analysed in this study allows the accumulation of QTL mapping experiments, providing the initial step for a quantitative, methodical exploitation of untapped diversity in producing a better wheat.

Inheritance of fuzz and lint fiber initiation of cotton (Gossypium hirsutum L.)

Fibers of cultivated cotton are classified into two types, lint and fuzz. Fuzzless seeds are cotton seeds lacking fuzz on almost the whole surface of the seed except for small amounts of fuzz at the micropylar or chalazal regions. Fuzzless seeds provide a number of advantages such as avoiding delintation processes and increasing the quality of feed rations. Therefore, it is important to develop varieties having seeds without fuzz but with high fiber ratios. This study, in which three different F2 populations have been developed by using three distinct genotypes as parents, was conducted to investigate the quantitative inheritance of fuzz and fiber formation and to generate a genetic mapping population. F2 populations showed 3:1 segregation for fuzz initiation and 15:1 for fiber initiation, meaning that fuzz initiation was controlled by one dominant gene while fiber initiation was controlled by two dominant genes. The ranges were between 2% and 40% for gin turnout, between 0 and 0.04 g for lint index, and between 5.5 and 15.9 g for seed index in the Stoneville 453 × PI 528429 population, while the same characters ranged from 2% to 36%, from 0 to 0.046 g, and from 9.45 to 15.42 g, respectively, for the PI 528429 × Stoneville 453 population. The last F2 population (Fiberless × Stoneville 453) showed segregation values that varied between 0% and 42% for gin turnout, between 0 and 0.045 g for lint index, and between 5.56 and 12.98 g for seed index. Additionally, there was a significant (P < 0.01) and positive correlation between gin turn out and lint index, between seed index and lint index, and between fuzz and gin turn out and lint index. These populations will provide a valuable genetic resource for fiber and fuzz initiation as well as quantitative trait locus mapping experiments.

Plasticity of primary and secondary growth dynamics in Eucalyptushybrids: a quantitative genetics and QTL mapping perspective

BackgroundThe genetic basis of growth traits has been widely studied in forest trees. Quantitative trait locus (QTL) studies have highlighted the presence of both stable and unstable genomic regions accounting for biomass production with respect to tree age and genetic background, but results remain scarce regarding the interplay between QTLs and the environment. In this study, our main objective was to dissect the genetic architecture of the growth trajectory with emphasis on genotype x environment interaction by measuring primary and secondary growth covering intervals connected with environmental variations.ResultsThree different trials with the same family of Eucalyptus urophylla x E. grandis hybrids (with different genotypes) were planted in the Republic of Congo, corresponding to two QTL mapping experiments and one clonal test. Height and radial growths were monitored at regular intervals from the seedling stage to five years old. The correlation between growth increments and an aridity index revealed that growth before two years old (r = 0.5; 0.69) was more responsive to changes in water availability than late growth (r = 0.39; 0.42) for both height and circumference. We found a regular increase in heritability with time for cumulative growth for both height [0.06 - 0.33] and circumference [0.06 - 0.38]. Heritabilities for incremental growth were more heterogeneous over time even if ranges of variation were similar (height [0-0.31]; circumference [0.19 to 0.48]). Within the trials, QTL analysis revealed collocations between primary and secondary growth QTLs as well as between early growth increments and final growth QTLs. Between trials, few common QTLs were detected highlighting a strong environmental effect on the genetic architecture of growth, validated by significant QTL x E interactions.ConclusionThese results suggest that early growth responses to water availability determine the genetic architecture of total growth at the mature stage and highlight the importance of considering growth as a composite trait (such as yields for annual plants) for a better understanding of its genetic bases.

Effects of QTL parameters and marker density on efficiency of Haley–Knott regression interval mapping of QTL with complex traits and use of artificial neural network for prediction of the efficiency of HK method in livestock

Dominance effect refers to the allele interaction in a locus. In this study, different portions of dominance standard deviations underlying quantitative trait loci (QTL) effect were considered. The F2 design is frequently employed in QTL mapping experiments using Haley and Knott regression method for QTL mapping analysis. This simulation study is carried out to consider the effect of the total standard deviation of QTL (SDQ) with different portions of additive/dominance effects in the context of different levels of population size, marker spacing and relative position of QTL from marker bracket on power of detecting QTL, precision of estimated QTL position and additive and dominance effects. The other aims of the study were to design an optimal artificial neural network (ANN) model to predict Haley–Knott (HK) results for more combinations of simulated parameters. SDQ of QTL strongly affected the power of QTL detection, therefore, in every combination of other parameters when SDQ is either 0.5 or 0.8, power was 100%. In all scenarios, the power increased when the ratio of additive and dominant SD of QTL effects was low or high (0.25 or 0.75). Increase of additive effect compared with the dominance effect decreased the precision of QTL location. Precision of estimated additive effect and dominance effect was good but precision of dominance effect was more affected by the considered parameter combinations than the additive effect. This study developed an ANN model with minimum dimensions and minimum errors for prediction of efficiency parameters of HK method given the simulated parameters. Moreover, for the first time, this study shows the use of trained ANN model for prediction of large-scale combinations of simulated parameters.

The contribution of dominance to the understanding of quantitative genetic variation

Knowledge of the genetic architecture of a quantitative trait is useful to adjust methods for the prediction of genomic breeding values and to discover the extent to which common assumptions in quantitative trait locus (QTL) mapping experiments and breeding value estimation are violated. It also affects our ability to predict the long-term response of selection. In this paper, we focus on additive and dominance effects of QTL. We derive formulae that can be used to estimate the number of QTLs that affect a quantitative trait and parameters of the distribution of their additive and dominance effects from variance components, inbreeding depression and results from QTL mapping experiments. It is shown that a lower bound for the number of QTLs depends on the ratio of squared inbreeding depression to dominance variance. That is, high inbreeding depression must be due to a sufficient number of QTLs because otherwise the dominance variance would exceed the true value. Moreover, the second moment of the dominance coefficient depends only on the ratio of dominance variance to additive variance and on the dependency between additive effects and dominance coefficients. This has implications on the relative frequency of overdominant alleles. It is also demonstrated how the expected number of large QTLs determines the shape of the distribution of additive effects. The formulae are applied to milk yield and productive life in Holstein cattle. Possible sources for a potential bias of the results are discussed.

Linkage graph analysis: A linkage-group-based QTL synthesis analysis approach

Consensus quantitative trait loci (QTL) in meta-analysis of multiple independent QTL mapping experiments provides a strong foundation for marker-assisted selection and gene cloning. However, meta-analysis suffers from the lack of available genomic information and the results vary when different reference linkage maps are used. Here, to overcome these limitations, we propose a linkage-group-based QTL synthesis analysis approach that we have named linkage graph analysis. First, a graph model is constructed from derived linkage groups. Next, an unsupervised classification approach is used to obtain marker intervals with co-segregating patterns among multiple genomes. Finally, a frequent itemset mining technique is used to identify the markers (or intervals) closely linked to the QTL. The proposed method was validated by one Monte Carlo simulation study and by real data analysis of cotton genomes. Two major advantages of the new method are: (i) A reference linkage group is not required; (ii) the effect of the initial QTL is reduced because false QTLs can be detected and excluded from the dataset. The ability to reliably identify the markers associated with a true QTL is valuable in crop breeding.

Mapping quantitative trait loci using the MCMC procedure in SAS.

The MCMC procedure in SAS (called PROC MCMC) is particularly designed for Bayesian analysis using the Markov chain Monte Carlo (MCMC) algorithm. The program is sufficiently general to handle very complicated statistical models and arbitrary prior distributions. This study introduces the SAS/MCMC procedure and demonstrates the application of the program to quantitative trait locus (QTL) mapping. A real life QTL mapping experiment in wheat female fertility trait was used as an example for the demonstration. The fertility trait phenotypes were described under three different models: (1) the Poisson model, (2) the Bernoulli model and (3) the zero-truncated Poisson model. One QTL was identified on the second chromosome. This QTL appears to control the switch of seed-producing ability of female plants but does not affect the number of seeds produced once the switch is turned on.

Quantitative trait locus and haplotype mapping in closely related inbred strains identifies a locus for open field behavior

Quantitative trait locus (QTL) mapping in the mouse typically utilizes inbred strains that exhibit significant genetic and phenotypic diversity. The development of dense SNP panels in a large number of inbred strains has eliminated the need to maximize genetic diversity in QTL studies as plenty of SNP markers are now available for almost any combination of strains. We conducted a QTL mapping experiment using both a backcross (N(2)) and an intercross (F(2)) between two genetically similar inbred mouse strains: C57BL/6J (B6) and C57L/J (C57). A set of additive QTLs for activity behaviors was identified on Chrs 1, 9, 13, and 15. We also identified additive QTLs for anxiety-related behaviors on Chrs 7, 9, and 16. A QTL on Chr 11 is sex-specific, and we revealed pairwise interactions between QTLs on Chrs 1 and 13 and Chrs 10 and 18. The Chr 9 activity QTL accounts for the largest amount of phenotypic variance and was not present in our recent analysis of a B6 x C58/J (C58) intercross (Bailey et al. in Genes Brain Behav 7:761-769, 2008). To narrow this QTL interval, we used a dense SNP haplotype map with over 7 million real and imputed SNP markers across 74 inbred mouse strains (Szatkiewicz et al. in Mamm Genome 19(3):199-208, 2008). Evaluation of shared and divergent haplotype blocks among B6, C57, and C58 strains narrowed the Chr 9 QTL interval considerably and highlights the utility of QTL mapping in closely related inbred strains.

Optimum allocation of resources for QTL detection using a nested association mapping strategy in maize

In quantitative trait locus (QTL) mapping studies, it is mandatory that the available financial resources are spent in such a way that the power for detection of QTL is maximized. The objective of this study was to optimize for three different fixed budgets the power of QTL detection 1 − β* in recombinant inbred line (RIL) populations derived from a nested design by varying (1) the genetic complexity of the trait, (2) the costs for developing, genotyping, and phenotyping RILs, (3) the total number of RILs, and (4) the number of environments and replications per environment used for phenotyping. Our computer simulations were based on empirical data of 653 single nucleotide polymorphism markers of 26 diverse maize inbred lines which were selected on the basis of 100 simple sequence repeat markers out of a worldwide sample of 260 maize inbreds to capture the maximum genetic diversity. For the standard scenario of costs, the optimum number of test environments (E opt) ranged across the examined total budgets from 7 to 19 in the scenarios with 25 QTL. In comparison, the E opt values observed for the scenarios with 50 and 100 QTL were slightly higher. Our finding of differences in 1 − β* estimates between experiments with optimally and sub-optimally allocated resources illustrated the potential to improve the power for QTL detection without increasing the total resources necessary for a QTL mapping experiment. Furthermore, the results of our study indicated that also in studies using the latest genomics tools to dissect quantitative traits, it is required to evaluate the individuals of the mapping population in a high number of environments with a high number of replications per environment.

Mapping quantitative trait Loci using distorted markers.

Quantitative trait locus (QTL) mapping is usually performed using markers that follow a Mendelian segregation ratio. We developed a new method of QTL mapping that can use markers with segregation distortion (non-Mendelian markers). An EM (expectation-maximization) algorithm is used to estimate QTL and SDL (segregation distortion loci) parameters. The joint analysis of QTL and SDL is particularly useful for selective genotyping. Application of the joint analysis is demonstrated using a real life data from a wheat QTL mapping experiment.

Using gene expression databases for classical trait QTL candidate gene discovery in the BXD recombinant inbred genetic reference population: Mouse forebrain weight

BackgroundSuccessful strategies for QTL gene identification benefit from combined experimental and bioinformatic approaches. Unique design aspects of the BXD recombinant inbred line mapping panel allow use of archived gene microarray expression data to filter likely from unlikely candidates. This prompted us to propose a simple five-filter protocol for candidate nomination. To filter more likely from less likely candidates, we required candidate genes near to the QTL to have mRNA abundance that correlated with the phenotype among the BXD lines as well as differed between the parental lines C57BL/6J and DBA/2J. We also required verification of mRNA abundance by an independent method, and finally we required either differences in protein levels or confirmed DNA sequence differences.ResultsQTL mapping of mouse forebrain weight in 34 BXD RI lines found significant association on chromosomes 1 and 11, with each C57BL/6J allele increasing weight by more than half a standard deviation. The intersection of gene lists that were within ± 10 Mb of the strongest associated location, that had forebrain mRNA abundance correlated with forebrain weight among the BXD, and that had forebrain mRNA abundance differing between C57BL/6J and DBA/2J, produced two candidates, Tnni1 (troponin 1) and Asb3 (ankyrin repeat and SOCS box-containing protein 3). Quantitative RT-PCR confirmed the direction of an increased expression in C57BL/6J genotype over the DBA/2J genotype for both genes, a difference that translated to a 2-fold difference in Asb3 protein. Although Tnni1 protein differences could not be confirmed, a 273 bp indel polymorphism was discovered 1 Kb upstream of the transcription start site.ConclusionDelivery of well supported candidate genes following a single quantitative trait locus mapping experiment is difficult. However, by combining available gene expression data with QTL mapping, we illustrated a five-filter protocol that nominated Asb3 and Tnni1 as candidates affecting increased mouse forebrain weight. We recommend our approach when (1) investigators are working with phenotypic differences between C57BL/6J and DBA/2J, and (2) gene expression data are available on that relate to the phenotype of interest. Under these circumstances, measurement of the phenotype in the BXD lines will likely also deliver excellent candidate genes.

MetaQTL: a package of new computational methods for the meta-analysis of QTL mapping experiments.

BackgroundIntegration of multiple results from Quantitative Trait Loci (QTL) studies is a key point to understand the genetic determinism of complex traits. Up to now many efforts have been made by public database developers to facilitate the storage, compilation and visualization of multiple QTL mapping experiment results. However, studying the congruency between these results still remains a complex task. Presently, the few computational and statistical frameworks to do so are mainly based on empirical methods (e.g. consensus genetic maps are generally built by iterative projection).ResultsIn this article, we present a new computational and statistical package, called MetaQTL, for carrying out whole-genome meta-analysis of QTL mapping experiments. Contrary to existing methods, MetaQTL offers a complete statistical process to establish a consensus model for both the marker and the QTL positions on the whole genome. First, MetaQTL implements a new statistical approach to merge multiple distinct genetic maps into a single consensus map which is optimal in terms of weighted least squares and can be used to investigate recombination rate heterogeneity between studies. Secondly, assuming that QTL can be projected on the consensus map, MetaQTL offers a new clustering approach based on a Gaussian mixture model to decide how many QTL underly the distribution of the observed QTL.ConclusionWe demonstrate using simulations that the usual model choice criteria from mixture model literature perform relatively well in this context. As expected, simulations also show that this new clustering algorithm leads to a reduction in the length of the confidence interval of QTL location provided that across studies there are enough observed QTL for each underlying true QTL location. The usefulness of our approach is illustrated on published QTL detection results of flowering time in maize. Finally, MetaQTL is freely available at .

Gene–environment interactions in complex diseases: genetic models and methods for QTL mapping in multiple half-sib populations

An interval quantitative trait locus (QTL) mapping method for complex polygenic diseases (as binary traits) showing QTL by environment interactions (QEI) was developed for outbred populations on a within-family basis. The main objectives, within the above context, were to investigate selection of genetic models and to compare liability or generalized interval mapping (GIM) and linear regression interval mapping (RIM) methods. Two different genetic models were used: one with main QTL and QEI effects (QEI model) and the other with only a main QTL effect (QTL model). Over 30 types of binary disease data as well as six types of continuous data were simulated and analysed by RIM and GIM. Using table values for significance testing, results show that RIM had an increased false detection rate (FDR) for testing interactions which was attributable to scale effects on the binary scale. GIM did not suffer from a high FDR for testing interactions. The use of empirical thresholds, which effectively means higher thresholds for RIM for testing interactions, could repair this increased FDR for RIM, but such empirical thresholds would have to be derived for each case because the amount of FDR depends on the incidence on the binary scale. RIM still suffered from higher biases (15-100% over- or under-estimation of true values) and high standard errors in QTL variance and location estimates than GIM for QEI models. Hence GIM is recommended for disease QTL mapping with QEI. In the presence of QEI, the model including QEI has more power (20-80% increase) to detect the QTL when the average QTL effect is small (in a situation where the model with a main QTL only is not too powerful). Top-down model selection is proposed in which a full test for QEI is conducted first and then the model is subsequently simplified. Methods and results will be applicable to human, plant and animal QTL mapping experiments.

Effects of zinc deficiency on rice growth and genetic factors contributing to tolerance.

Zinc (Zn) deficiency is the most widespread micronutrient disorder in rice (Oryza sativa), but efforts to develop cultivars with improved tolerance have been hampered by insufficient understanding of genetic factors contributing to tolerance. The objective of this paper was to examine alternative evaluation methods and to identify the most informative traits that would provide realistic information for rice breeders and to map quantitative trait loci (QTLs) associated with tolerance. Screening experiments in low-Zn nutrient solution and in a Zn-deficient field did not produce similar tolerance rankings in a set of segregating lines, which suggested that rhizosphere effects were of greater importance for lowland rice than internal Zn efficiency. The most severe symptom in the field was high plant mortality. The occurrence of leaf bronzing, usually regarded as indicative of susceptibility, did not necessarily concur with high plant mortality, which implied that both were under independent genetic control. The QTL mapping experiment conducted in the field with a population derived from a cross of IR74 (intolerant) with Jalmagna (tolerant) largely confirmed this. Four QTLs associated with plant mortality were detected, and only one of those colocalized with one of the four QTLs detected for leaf bronzing. The two most influential QTLs for plant mortality were detected on chromosomes 2 and 12. They explained 16.6% and 24.2% of the variation, and alleles of the tolerant donor parent Jalmagna reduced mortality by 16.6% and 14.8%, respectively. QTLs for plant mortality acted in a purely additive manner, whereas digenic epistatic interactions were important for leaf bronzing.

Power of QTL mapping experiments in commercial Atlantic salmon populations, exploiting linkage and linkage disequilibrium and effect of limited recombination in males

Whereas detection and positioning of genes that affect quantitative traits (quantitative trait loci (QTL)) using linkage mapping uses only information from recombinants in the genotyped generations, linkage disequilibrium (LD) mapping uses historical recombinants. Thus, whereas linkage mapping requires large family sizes to detect and accurately position QTL, LD mapping is more dependent on the number of families sampled from the population. In commercial Atlantic salmon breeding programmes, only a small number of individuals per family are routinely phenotyped for traits such as disease resistance and meat colour. In this paper, we assess the power and accuracy of combined linkage disequilibrium linkage analysis (LDLA) to detect QTL in the commercial population using simulation. When 15 half-sib sire families (each sire mated to 30 dams, each dam with 10 progeny) were sampled from the population for genotyping, we were able to detect a QTL explaining 10% of the phenotypic variance in 85% of replicates and position this QTL within 3 cM of the true position in 70% of replicates. When recombination was absent in males, a feature of the salmon genome, power to detect QTL increased; however, the accuracy of positioning the QTL was decreased. By increasing the number of sire families sampled from the population to be genotyped to 30, we were able to increase both the proportion of QTL detected and correctly positioned (even with no recombination in males). QTL with much smaller effect could also be detected. The results suggest that even with the existing recording structure in commercial salmon breeding programmes, there is considerable power to detect and accurately position QTL using LDLA.

Models and partition of variance for quantitative trait loci with epistasis and linkage disequilibrium.

BackgroundA genetic model about quantitative trait loci (QTL) provides a basis to interpret the genetic basis of quantitative traits in a study population, such as additive, dominance and epistatic effects of QTL and the partition of genetic variance. The standard quantitative genetics model is based on the least squares partition of genetic effects and also genetic variance in an equilibrium population. However, over years many specialized QTL models have also been proposed for applications in some specific populations. How are these models related? How to analyze and partition a QTL model and genetic variance when both epistasis and linkage disequilibrium are considered?ResultsStarting from the classical description of Cockerham genetic model, we first represent the model in a multiple regression setting by using indicator variables to describe the segregation of QTL alleles. In this setting, the definition of additive, dominance and epistatic effects of QTL and the basis for the partition of genetic variance are elaborated. We then build the connection between this general genetic model and a few specialized models (a haploid model, a diploid F2 model and a general two-allele model), and derive the genetic effects and partition of genetic variance for multiple QTL with epistasis and linkage disequilibrium for these specialized models.ConclusionIn this paper, we study extensively the composition and property of the genetic model parameters, such as genetic effects and partition of genetic variance, when both epistasis and linkage disequilibrium are considered. This is the first time that both epistasis and linkage disequilibrium are considered in modeling multiple QTL. This analysis would help us to understand the structure of genetic parameters and relationship of various genetic quantities, such as allelic frequencies and linkage disequilibrium, on the definition of genetic effects, and will also help us to understand and properly interpret estimates of the genetic effects and variance components in a QTL mapping experiment.