Quantitative trait mapping in a diallel cross of recombinant inbred lines

A recombinant inbred intercross (RIX) is created by generating diallel F₁ progeny from one or more panels of recombinant inbred (RI) strains. This design was originally introduced to extend the power of small RI panels for the confirmation of quantitative trait loci (QTL) provisionally detected in a parental RI set. For example, the set of 13 C × B (C57BL/6ByJ × BALB/cByJ) RI strains can, in principle, be supplemented with 156 isogenic F₁s. We describe and test a method of analysis, based on a linear mixed model, that accounts for the correlation structure of RIX populations. This model suggests a novel permutation algorithm that is needed to obtain appropriate threshold values for genome-wide scans of an RIX population. Despite the combinational multiplication of unique genotypes that can be generated using an RIX design, the effective sample size of the RIX population is limited by the number of progenitor RI genomes that are combined. When using small RI panels such as the C × B there appears to be only modest advantage of the RIX design when compared with the original RI panel for detecting QTLs with additive effects. The RIX, however, does have an inherent ability to detect dominance effects, and, unlike RI strains, the RIX progeny are genetically reproducible but are not fully inbred, providing somewhat more natural genetic context. We suggest a breeding strategy, the balanced partial RIX, that balances the advantage of RI and RIX designs. This involves the use of a partial RIX population derived from a large RI panel in which the available information is maximized by minimizing correlations among RIX progeny.     

Bayesian multiple quantitative trait loci mapping for recombinant inbred intercrosses

The Collaborative Cross (CC) is a renewable mouse resource that mimics the genetic diversity in humans. The recombinant inbred intercrosses (RIX) generated from CC recombinant inbred (RI) lines share similar genetic structures to those of F(2) individuals. In contrast to F(2) mice, genotypes of RIX can be inferred from the genotypes of their RI parents and can be produced repeatedly. Also, RIX mice do not typically share the same degree of relatedness. This unbalanced genetic relatedness requires careful statistical modeling to avoid a large number of false positive findings. For complex traits, mapping multiple genes simultaneously is arguably more powerful than mapping one gene at a time. In this article, we describe how we have developed a Bayesian quantitative trait locus (QTL) mapping method that simultaneously deals with the special genetic architecture of RIX and maps multiple genes. The performance of the proposed method is evaluated by extensive simulations. In addition, for a given set of RI lines, there are numerous ways to generate RIX samples. To provide a general guideline on future RIX studies, we compare several RIX designs through simulations.     

Varying coefficient models for mapping quantitative trait loci using recombinant inbred intercrosses

There has been a great deal of interest in the development of methodologies to map quantitative trait loci (QTL) using experimental crosses in the last 2 decades. Experimental crosses in animal and plant sciences provide important data sources for mapping QTL through linkage analysis. The Collaborative Cross (CC) is a renewable mouse resource that is generated from eight genetically diverse founder strains to mimic the genetic diversity in humans. The recombinant inbred intercrosses (RIX) generated from CC recombinant inbred (RI) lines share similar genetic structures of F(2) individuals but with up to eight alleles segregating at any one locus. In contrast to F(2) mice, genotypes of RIX can be inferred from the genotypes of their RI parents and can be produced repeatedly. Also, RIX mice typically do not share the same degree of relatedness. This unbalanced genetic relatedness requires careful statistical modeling to avoid false-positive findings. Many quantitative traits are inherently complex with genetic effects varying with other covariates, such as age. For such complex traits, if phenotype data can be collected over a wide range of ages across study subjects, their dynamic genetic patterns can be investigated. Parametric functions, such as sigmoidal or logistic functions, have been used for such purpose. In this article, we propose a flexible nonparametric time-varying coefficient QTL mapping method for RIX data. Our method allows the QTL effects to evolve with time and naturally extends classical parametric QTL mapping methods. We model the varying genetic effects nonparametrically with the B-spline bases. Our model investigates gene-by-time interactions for RIX data in a very flexible nonparametric fashion. Simulation results indicate that the varying coefficient QTL mapping has higher power and mapping precision compared to parametric models when the assumption of constant genetic effects fails. We also apply a modified permutation procedure to control overall significance level.     

Assessing the significance of quantitative trait loci in replicable mapping populations

Replicable populations, such as panels of recombinant inbred or doubled haploid lines, are convenient resources for the mapping of QTL. To increase mapping power, replications are often collected within each RI line and a common way to analyze such data is to include in the QTL model only a single measurement from each line that represents the average among the replicates (a line means model). An obvious, but seldom explored, alternative, is to include every replicate in the model (a full data model). Here, we use simulations to compare these two approaches. Further, we propose an extension of the standard permutation procedure that is required to correctly control the type I error in mapping populations with nested structure.     

A general mixture model approach for mapping quantitative trait loci from diverse cross designs involving multiple inbred lines

Most current statistical methods developed for mapping quantitative trait loci (QTL) based on inbred line designs apply to crosses from two inbred lines. Analysis of QTL in these crosses is restricted by the parental genetic differences between lines. Crosses from multiple inbred lines or multiple families are common in plant and animal breeding programmes, and can be used to increase the efficiency of a QTL mapping study. A general statistical method using mixture model procedures and the EM algorithm is developed for mapping QTL from various cross designs of multiple inbred lines. The general procedure features three cross design matrices, W, that define the contribution of parental lines to a particular cross and a genetic design matrix, D, that specifies the genetic model used in multiple line crosses. By appropriately specifying W matrices, the statistical method can be applied to various cross designs, such as diallel, factorial, cyclic, parallel or arbitrary-pattern cross designs with two or multiple parental lines. Also, with appropriate specification for the D matrix, the method can be used to analyse different kinds of cross populations, such as F2 backcross, four-way cross and mixed crosses (e.g. combining backcross and F2). Simulation studies were conducted to explore the properties of the method, and confirmed its applicability to diverse experimental designs.     

The Power of QTL Mapping with RILs

QTL (quantitative trait loci) mapping is commonly used to identify genetic regions responsible to important phenotype variation. A common strategy of QTL mapping is to use recombinant inbred lines (RILs), which are usually established by several generations of inbreeding of an F1 population (usually up to F6 or F7 populations). As this inbreeding process involves a large amount of labor, we are particularly interested in the effect of the number of inbreeding generations on the power of QTL mapping; a part of the labor could be saved if a smaller number of inbreeding provides sufficient power. By using simulations, we investigated the performance of QTL mapping with recombinant inbred lines (RILs). As expected, we found that the power of F4 population could be almost comparable to that of F6 and F7 populations. A potential problem in using F4 population is that a large proportion of RILs are heterozygotes. We here introduced a new method to partly relax this problem. The performance of this method was verified by simulations with a wide range of parameters including the size of the segregation population, recombination rate, genome size and the density of markers. We found our method works better than the commonly used standard method especially when there are a number of heterozygous markers. Our results imply that in most cases, QTL mapping does not necessarily require RILs at F6 or F7 generations; rather, F4 (or even F3) populations would be almost as useful as F6 or F7 populations. Because the cost to establish a number of RILs for many generations is enormous, this finding will cause a reduction in the cost of QTL mapping, thereby accelerating gene mapping in many species.     

Quantitative trait loci (QTL) detection in multicross inbred designs: recovering QTL identical-by-descent status information from marker data

Mapping quantitative trait loci in plants is usually conducted using a population derived from a cross between two inbred lines. The power of such QTL detection and the parameter estimates depend largely on the choice of the two parental lines. Thus, the QTL detected in such populations represent only a small part of the genetic architecture of the trait. In addition, the effects of only two alleles are characterized, which is of limited interest to the breeder, while common pedigree breeding material remains unexploited for QTL mapping. In this study, we extend QTL mapping methodology to a generalized framework, based on a two-step IBD variance component approach, applicable to any type of breeding population obtained from inbred parents. We then investigate with simulated data mimicking conventional breeding programs the influence of different estimates of the IBD values on the power of QTL detection. The proposed method would provide an alternative to the development of specifically designed recombinant populations, by utilizing the genetic variation actually managed by plant breeders. The use of these detected QTL in assisting breeding would thus be facilitated.     

New quantitative trait loci for the genetic variance in circadian period of locomotor activity between inbred strains of mice

Provisional quantitative trait loci (QTL) for circadian locomotor period and wheel-running period have been identified in recombinant inbred (RI) mouse strains. To confirm those QTL and identify new ones, the genetic component of variance of the circadian period was partitioned among an F2 intercross of RI mouse strains (BXD19 and CXB07). First, a genomic survey using 108 SSLP markers with an average spacing of 15 cM was carried out in a population of 259 (BXD19 x CXB07)F2 animals. The genome-wide survey identified two significant QTL for period of locomotor activity measured by infrared photobeam crossings on mouse chromosomes 1 (lod score 5.66) and 14 (lod score 4.33). The QTL on distal chromosome 1 confirmed a previous report based on congenic B6.D2-Mtv7a/Ty mice. Lod scores greater than 2.0 were found on chromosomes 1, 2, 6, 12, 13, and 14. In a targeted extension study, additional genotyping was performed on these chromosomes in the full sample of 341 F2 progeny. The 6 chromosome-wide surveys identified 3 additional QTL on mouse chromosomes 6, 12, and 13. The QTL on chromosome 12 overlaps with circadian period QTL identified in several prior studies. For wheel-running period, the chromosome-wide surveys identified QTL on chromosomes 2 and 13 and one highly suggestive QTL on proximal chromosome 1. The results are compared to other published studies of QTL of circadian period.     

Correlation of susceptibility to 6-aminonicotinamide and hydrocortisone-induced cleft palate

Our previous genome-wide Quantitative Trait Locus (QTL) mapping study using mouse A/J by C57BL/6J recombinant inbred (RI) lines suggested several chromosomal regions contain genes influencing susceptibility to phenytoin (PT)-induced cleft lip with or without cleft palate [CL(P)] and 6-aminonicotinamide (6-AN)-induced isolated cleft palate (CP). Importantly, the same chromosomal regions but different RI parental strain alleles were sometimes implicated in susceptibility to these different kinds of orofacial clefting. Here we report the susceptibility to hydrocortisone (HC)-induced CP in these RI lines. We treated pregnant females with HC and studied the incidence of CP in day 17 fetuses. RI lines showed highly correlated responses to HC and 6-AN. The A/J parental line and five RI lines showed very high levels of clefting in response to both of these teratogens. The C57BL/6J parental line and five other RI lines exhibited low incidence of CP for these teratogens. In contrast, there was no significant correlation between incidence of PT-induced CL(P) and HC-induced CP.     

Something Old and Something New: Wedding Recombinant Inbred Lines with Traditional Line Cross Analysis Increases Power to Describe Gene Interactions

In this paper we present a novel approach to quantifying genetic architecture that combines recombinant inbred lines (RIL) with line cross analysis (LCA). LCA is a method of quantifying directional genetic effects (i.e. summed effects of all loci) that differentiate two parental lines. Directional genetic effects are thought to be critical components of genetic architecture for the long term response to selection and as a cause of inbreeding depression. LCA typically begins with two inbred parental lines that are crossed to produce several generations such as F1, F2, and backcrosses to each parent. When a RIL population (founded from the same P1 and P2 as was used to found the line cross population) is added to the LCA, the sampling variance of several nonadditive genetic effect estimates is greatly reduced. Specifically, estimates of directional dominance, additive x additive, and dominance x dominance epistatic effects are reduced by 92%, 94%, and 56% respectively. The RIL population can be simultaneously used for QTL identification, thus uncovering the effects of specific loci or genomic regions as elements of genetic architecture. LCA and QTL mapping with RIL provide two qualitatively different measures of genetic architecture with the potential to overcome weaknesses of each approach alone. This approach provides cross-validation of the estimates of additive and additive x additive effects, much smaller confidence intervals on dominance, additive x additive and dominance x dominance estimates, qualitatively different measures of genetic architecture, and the potential when used together to balance the weaknesses of LCA or RIL QTL analyses when used alone.     

Genetic control of neuroadapted sindbis virus replication in female mice maps to chromosome 2 and associates with paralysis and mortality

Neuroadapted Sindbis virus (NSV) infection of mice causes hindlimb paralysis and 100% mortality in the C57BL/6 mouse strain, while adults of the BALB/cBy mouse strain are resistant to fatal encephalomyelitis. Levels of viral RNA are higher in the brains of infected C57BL/6 mice than in BALB/cBy mice (D. C. Thach et al., J. Virol. 74:6156-6161, 2000). These phenotypic differences between the two strains allowed us to map genetic loci involved in mouse susceptibility to NSV and to find relationships between mortality, paralysis, and viral RNA levels. Analysis of percent mortality in H2-congenic and F(1) mice suggested that the H2 locus, sex linkage, and imprinting were not involved in determining susceptibility and that resistance was partially dominant over susceptibility. Segregation analysis using CXB recombinant inbred (RI) mice indicated that the percent mortality was multigenic. Interval mapping detected a suggestive quantitative trait locus (QTL) on chromosome 2 near marker D2Mit447. Analysis of paralysis in the RI mice detected the same suggestive QTL. Viral RNA level in F(1) mice was intermediate. Interval mapping using viral RNA levels in RI mice detected a significant QTL near marker D2Mit447 that explained 69% of the genetic variance. This QTL was confirmed in F2 mice and was designated as Nsv1. Viral RNA level, percent paralyzed, and percent mortality were linearly correlated (r = 0.8 to 0.9). These results indicate that mortality, paralysis, and viral RNA levels are related complex traits and that Nsv1 controls early viral load and determines the likelihood of paralysis and death.     

Quantitative trait loci for thermotolerance phenotypes in Drosophila melanogaster

For insects, temperature is a major environmental variable that can influence an individual's behavioral activities and fitness. Drosophila melanogaster is a cosmopolitan species that has had great success in adapting to and colonizing diverse thermal niches. This adaptation and colonization has resulted in complex patterns of genetic variation in thermotolerance phenotypes in nature. Although extensive work has been conducted documenting patterns of genetic variation, substantially less is known about the genomic regions or genes that underlie this ecologically and evolutionarily important genetic variation. To begin to understand and identify the genes controlling thermotolerance phenotypes, we have used a mapping population of recombinant inbred (RI) lines to map quantitative trait loci (QTL) that affect variation in both heat- and cold-stress resistance. The mapping population was derived from a cross between two lines of D. melanogaster (Oregon-R and 2b) that were not selected for thermotolerance phenotypes, but exhibit significant genetic divergence for both phenotypes. Using a design in which each RI line was backcrossed to both parental lines, we mapped seven QTL affecting thermotolerance on the second and third chromosomes. Three of the QTL influence cold-stress resistance and four affect heat-stress resistance. Most of the QTL were trait or sex specific, suggesting that overlapping but generally unique genetic architectures underlie resistance to low- and high-temperature extremes. Each QTL explained between 5 and 14% of the genetic variance among lines, and degrees of dominance ranged from completely additive to partial dominance. Potential thermotolerance candidate loci contained within our QTL regions are identified and discussed.     

A new set of BXD recombinant inbred lines from advanced intercross populations in mice

Background  

Recombinant inbred (RI) strains are an important resource for mapping complex traits in many species. While large RI panels are available for Arabidopsis, maize, C. elegans, and Drosophila, mouse RI panels typically consist of fewer than 30 lines. This is a severe constraint on the power and precision of mapping efforts and greatly hampers analysis of epistatic interactions.     

Combining data from multiple inbred line crosses improves the power and resolution of quantitative trait loci mapping

Rodent inbred line crosses are widely used to map genetic loci associated with complex traits. This approach has proven to be powerful for detecting quantitative trait loci (QTL); however, the resolution of QTL locations, typically approximately 20 cM, means that hundreds of genes are implicated as potential candidates. We describe analytical methods based on linear models to combine information available in two or more inbred line crosses. Our strategy is motivated by the hypothesis that common inbred strains of the laboratory mouse are derived from a limited ancestral gene pool and thus QTL detected in multiple crosses are likely to represent shared ancestral polymorphisms. We demonstrate that the combined-cross analysis can improve the power to detect weak QTL, can narrow support intervals for QTL regions, and can be used to separate multiple QTL that colocalize by chance. Moreover, combined-cross analysis can establish the allelic states of a QTL among a set of parental lines, thus providing critical information for narrowing QTL regions by haplotype analysis.     

Quantitative trait locus mapping using sets of near-isogenic lines: relative power comparisons and technical considerations

 The power to detect QTL using near-isogenic line (NIL) comparisons versus recombinant inbred (RI) populations was assessed. The power to detect QTL was found to be generally greater when using RI populations than when using NIL contrasts. Power to detect QTL with NIL contrasts never exceeded that of RI populations when the number of RI lines is maximized relative to replication of lines for a given number of experimental units. The relative power of NIL contrasts is highest for traits with high heritability and when a gain in precision is realized due to increased replication of entries. Although NIL populations are generally less powerful than RI populations of similar size, some practical considerations may enhance the value of these materials. Availability of NILs allows the genetic effect of a specific chromosome region to be determined by comparing two lines; all RI lines in a population need to be rescored for each new trait even if the effect of a specific chromosome region is suspected. NIL comparisons may allow genetic differences to be detected by visual inspection; genetic effects can only be expressed as means and variances with recombinant inbred populations. In summary, RI populations generally, and in some cases, substantially, provide better power for QTL detection than NIL comparisons. Practical considerations, however, indicate that many factors need to be considered when choosing a population structure to meet an experimental objective. Received: 12 December 1996 / Accepted: 21 March 1997     

QTL mapping of complex binary traits in an advanced intercross line

An advanced intercross line (AIL) is an easier and more cost-effective approach compared to recombinant inbred lines for fine mapping of quantitative trait loci (QTL) identified by F(2) designs. In an AIL, a complex binary trait can be mapped through analysis of either continuously distributed proxy traits for the liability of the binary trait or the liability itself, the latter presenting the greater statistical challenge. In another work, we successfully applied both approaches in an AIL to fine map previously identified QTL underlying anatomical parameters of the cardiac inter-atrial septum including patent foramen ovale. Here, we describe the statistical methods that we used to analyse complex binary traits in our AIL design. This is achieved using a likelihood-based method, with the expectation-maximisation algorithm allowing use of standard logistic regression methods for model fitting.     

Detection of QTLs for flowering date in three mapping populations of the model legume species Medicago truncatula

Adaptation to the environment and reproduction are dependent on the date of flowering in the season. The objectives of this paper were to evaluate the effect of photoperiod on flowering date of the model species for legume crops, Medicago truncatula and to describe genetic architecture of this trait in multiple mapping populations. The effect of photoperiod (12 and 18 h) was analysed on eight lines. Quantitative variation in three recombinant inbred lines (RILs) populations involving four parental lines was evaluated, and QTL detection was carried out. Flowering occurred earlier in long than in short photoperiods. Modelling the rate of progression to flowering with temperature and photoperiod gave high R(2), with line-specific parameters that indicated differential responses of the lines to both photoperiod and temperature. QTL detection showed a QTL on chromosome 7 that was common to all populations and seasons. Taking advantage of the multiple mapping populations, it was condensed into a single QTL with a support interval of only 0.9 cM. In a bioanalysis, six candidate genes were identified in this interval. This design also indicated other genomic regions that were involved in flowering date variation more specifically in one population or one season. The analysis on three different mapping populations detected more QTLs than on a single population, revealed more alleles and gave a more precise position of the QTLs that were common to several populations and/or seasons. Identification of candidate genes was a result of integration of QTL analysis and genomics in M. truncatula.     

High Resolution of Quantitative Traits into Multiple Loci via Interval Mapping

A very general method is described for multiple linear regression of a quantitative phenotype on genotype [putative quantitative trait loci (QTLs) and markers] in segregating generations obtained from line crosses. The method exploits two features, (a) the use of additional parental and F(1) data, which fixes the joint QTL effects and the environmental error, and (b) the use of markers as cofactors, which reduces the genetic background noise. As a result, a significant increase of QTL detection power is achieved in comparison with conventional QTL mapping. The core of the method is the completion of any missing genotypic (QTL and marker) observations, which is embedded in a general and simple expectation maximization (EM) algorithm to obtain maximum likelihood estimates of the model parameters. The method is described in detail for the analysis of an F(2) generation. Because of the generality of the approach, it is easily applicable to other generations, such as backcross progenies and recombinant inbred lines. An example is presented in which multiple QTLs for plant height in tomato are mapped in an F(2) progeny, using additional data from the parents and their F(1) progeny.     

Fine Mapping QTL for Drought Resistance Traits in Rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) Using Bulk Segregant Analysis

Drought stress is a major limitation to rice (Oryza sativa L.) yields and its stability, especially in rainfed conditions. Developing rice cultivars with inherent capacity to withstand drought stress would improve rainfed rice production. Mapping quantitative trait loci (QTLs) linked to drought resistance traits will help to develop rice cultivars suitable for water-limited environments through molecular marker-assisted selection (MAS) strategy. However, QTL mapping is usually carried out by genotyping large number of progenies, which is labour-intensive, time-consuming and cost-ineffective. Bulk segregant analysis (BSA) serves as an affordable strategy for mapping large effect QTLs by genotyping only the extreme phenotypes instead of the entire mapping population. We have previously mapped a QTL linked to leaf rolling and leaf drying in recombinant inbred (RI) lines derived from two locally adapted indica rice ecotypes viz., IR20/Nootripathu using BSA. Fine mapping the QTL will facilitate its application in MAS. BSA was done by bulking DNA of 10 drought-resistant and 12 drought-sensitive RI lines. Out of 343 rice microsatellites markers genotyped, RM8085 co-segregated among the RI lines constituting the respective bulks. RM8085 was mapped in the middle of the QTL region on chromosome 1 previously identified in these RI lines thus reducing the QTL interval from 7.9 to 3.8 cM. Further, the study showed that the region, RM212–RM302–RM8085–RM3825 on chromosome 1, harbours large effect QTLs for drought-resistance traits across several genetic backgrounds in rice. Thus, the QTL may be useful for drought resistance improvement in rice through MAS and map-based cloning.     

Quantitative trait loci mapping in F(2) crosses between outbred lines

We develop a mixed-model approach for QTL analysis in crosses between outbred lines that allows for QTL segregation within lines as well as for differences in mean QTL effects between lines. We also propose a method called "segment mapping" that is based in partitioning the genome in a series of segments. The expected change in mean according to percentage of breed origin, together with the genetic variance associated with each segment, is estimated using maximum likelihood. The method also allows the estimation of differences in additive variances between the parental lines. Completely fixed random and mixed models together with segment mapping are compared via simulation. The segment mapping and mixed-model behaviors are similar to those of classical methods, either the fixed or random models, under simple genetic models (a single QTL with alternative alleles fixed in each line), whereas they provide less biased estimates and have higher power than fixed or random models in more complex situations, i.e., when the QTL are segregating within the parental lines. The segment mapping approach is particularly useful to determining which chromosome regions are likely to contain QTL when these are linked.