A modified algorithm for the improvement of composite interval mapping

Composite interval mapping (CIM) is the most commonly used method for mapping quantitative trait loci (QTL) with populations derived from biparental crosses. However, the algorithm implemented in the popular QTL Cartographer software may not completely ensure all its advantageous properties. In addition, different background marker selection methods may give very different mapping results, and the nature of the preferred method is not clear. A modified algorithm called inclusive composite interval mapping (ICIM) is proposed in this article. In ICIM, marker selection is conducted only once through stepwise regression by considering all marker information simultaneously, and the phenotypic values are then adjusted by all markers retained in the regression equation except the two markers flanking the current mapping interval. The adjusted phenotypic values are finally used in interval mapping (IM). The modified algorithm has a simpler form than that used in CIM, but a faster convergence speed. ICIM retains all advantages of CIM over IM and avoids the possible increase of sampling variance and the complicated background marker selection process in CIM. Extensive simulations using two genomes and various genetic models indicated that ICIM has increased detection power, a reduced false detection rate, and less biased estimates of QTL effects.     

An Empirical Method for Establishing Positional Confidence Intervals Tailored for Composite Interval Mapping of QTL

Background

Improved genetic resolution and availability of sequenced genomes have made positional cloning of moderate-effect QTL realistic in several systems, emphasizing the need for precise and accurate derivation of positional confidence intervals (CIs) for QTL. Support interval (SI) methods based on the shape of the QTL likelihood curve have proven adequate for standard interval mapping, but have not been shown to be appropriate for use with composite interval mapping (CIM), which is one of the most commonly used QTL mapping methods.

Results

Based on a non-parametric confidence interval (NPCI) method designed for use with the Haley-Knott regression method for mapping QTL, a CIM-specific method (CIM-NPCI) was developed to appropriately account for the selection of background markers during analysis of bootstrap-resampled data sets. Coverage probabilities and interval widths resulting from use of the NPCI, SI, and CIM-NPCI methods were compared in a series of simulations analyzed via CIM, wherein four genetic effects were simulated in chromosomal regions with distinct marker densities while heritability was fixed at 0.6 for a population of 200 isolines. CIM-NPCIs consistently capture the simulated QTL across these conditions while slightly narrower SIs and NPCIs fail at unacceptably high rates, especially in genomic regions where marker density is high, which is increasingly common for real studies. The effects of a known CIM bias toward locating QTL peaks at markers were also investigated for each marker density case. Evaluation of sub-simulations that varied according to the positions of simulated effects relative to the nearest markers showed that the CIM-NPCI method overcomes this bias, offering an explanation for the improved coverage probabilities when marker densities are high.

Conclusions

Extensive simulation studies herein demonstrate that the QTL confidence interval methods typically used to positionally evaluate CIM results can be dramatically improved by accounting for the procedural complexity of CIM via an empirical approach, CIM-NPCI. Confidence intervals are a critical measure of QTL utility, but have received inadequate treatment due to a perception that QTL mapping is not sufficiently precise for procedural improvements to matter. Technological advances will continue to challenge this assumption, creating even more need for the current improvement to be refined.     

On the efficiency of composite interval mapping in an outbred population

Composite interval mapping (CIM) has been successfully applied to the detection of QTL in experimental animals and plants. However, practical analyses based on CIM have been reported mainly for populations derived from cross between inbred lines. There are few studies on QTL analyses with CIM in outbred populations. To evaluate the applicability of CIM to outbred populations is prerequisite for the fine mapping of QTL in industrial animals such as pig and chicken. Some markers are usually not fully informative in outbred populations. In application of CIM to outbred populations, the influence of inclusion of such uninformative markers used as covariates on the efficiency of CIM should be investigated. In this paper a least-squares method for CIM was formalized in an F(2) population derived by crossing two outbred lines. The efficiencies of CIM were evaluated for outbred populations in comparison with simple interval mapping (SIM) for several cases of marker informativeness using simulations. By incorporating markers linked to a tested position as well as those unlinked, CIM showed a higher efficiency to separate two linked QTL over SIM. The efficiency of dissection was enhanced as the marker informativeness was increased. The power of CIM to detect an isolated QTL was improved by excluding markers linked to a tested position from covariates and higher than SIM regardless of marker informativeness. In conclusion, CIM is a useful procedure for the analysis of QTL in outbred populations even under low marker informativeness.     

Quantitative genetics of vector competence for La Crosse virus and body size in Ochlerotatus hendersoni and Ochlerotatus triseriatus interspecific hybrids

La Crosse virus is a leading cause of pediatric encephalitis in the United States. The mosquito Ochlerotatus triseriatus is an efficient vector for La Crosse virus, whereas the closely related O. hendersoni transmits only at very low rates. Quantitative trait loci (QTL) affecting the ability to orally transmit this virus and adult body size were identified in 164 F(2) female individuals from interspecific crosses of O. hendersoni females and O. triseriatus males using a combination of composite interval mapping (CIM), interval mapping (IM) for binary traits, and single-marker mapping. For oral transmission (OT), no genome locations exceeded the 95% experimentwise threshold for declaring a QTL using IM, but single-marker analysis identified four independent regions significantly associated with OT that we considered as tentative QTL. With two QTL, an increase in OT was associated with alleles from the refractory vector, O. hendersoni, and likely reflect epistatic interactions between genes that were uncovered by our interspecific crosses. For body size, two QTL were identified using CIM and a third tentative QTL was identified using single-marker analysis. The genome regions associated with body size also contain three QTL controlling OT, suggesting that these regions contain either single genes with pleiotropic effects or multiple linked genes independently determining each trait.     

A comparison of regression interval mapping and multiple interval mapping for linked QTL

Regression interval mapping and multiple interval mapping are compared with regard to mapping linked quantitative trait loci (QTL) in inbred-line cross experiments. For that purpose, a simulation study was performed using genetic models with two linked QTL. Data were simulated for F(2) populations of different sizes and with all QTL and marker alleles fixed for alternative alleles in the parental lines. The criteria for comparison are power of QTL identification and the accuracy of the QTL position and effect estimates. Further, the estimates of the relative QTL variance are assessed. There are distinct differences in the QTL position estimates between the two methods. Multiple interval mapping tends to be more powerful as compared to regression interval mapping. Multiple interval mapping further leads to more accurate QTL position and QTL effect estimates. The superiority increased with wider marker intervals and larger population sizes. If QTL are in repulsion, the differences between the two methods are very pronounced. For both methods, the reduction of the marker interval size from 10 to 5 cM increases power and greatly improves QTL parameter estimates. This contrasts with findings in the literature for single QTL scenarios, where a marker density of 10 cM is generally considered as sufficient. The use of standard (asymptotic) statistical theory for the computation of the standard errors of the QTL position and effect estimates proves to give much too optimistic standard errors for regression interval mapping as well as for multiple interval mapping.     

Quantitative trait loci for the circadian clock in Neurospora crassa

Neurospora crassa has been a model organism for the study of circadian clocks for the past four decades. Among natural accessions of Neurospora crassa, there is significant variation in clock phenotypes. In an attempt to investigate natural allelic variants contributing to quantitative variation, we used a quantitative trait loci mapping approach to analyze three independent mapping populations whose progenitors were collected from geographically isolated locations. Two circadian clock phenotypes, free-running period and entrained phase, were evaluated in the 188 F(1) progeny of each mapping population. To identify the clock QTL, we applied two QTL mapping analyses: composite interval mapping (CIM) and Bayesian multiple QTL analysis (BMQ). When controlling false positive rates < or =0.05, BMQ appears to be the more sensitive of the two approaches. BMQ confirmed most of the QTL from CIM (18 QTL) and identified 23 additional QTL. While 13 QTL colocalize with previously identified clock genes, we identified 30 QTL that were not linked with any previously characterized clock genes. These are candidate regions where clock genes may be located and are expected to lead to new insights in clock regulation.     

Fine-mapping of quantitative trait loci in half-sib families using current recombinations

Two groups of methods are being developed to fine-map quantitative trait loci (QTLs): identity-by-descent methods or methods using historical recombinations, and genetic chromosome dissection methods or methods utilizing current recombinations. Here we propose two methods that fall into the second group: contrast mapping and substitution mapping. A QTL has previously been detected via linkage mapping in a half-sib design (granddaughter or daughter design), and sires (grandsires) likely to be heterozygous at the QTL have been identified. A sire (grandsire) and its recombinant offspring are then genotyped for a series of ordered markers spanning the initial marker interval. Offspring are grouped by paternal multi-marker haplotype with haplotypes differing in the location of the recombination event. In the contrast method, contrasts between the phenotypic averages of haplotypes or offspring groups are calculated which correspond to marker intervals within the original interval. The expected value of the contrast for the true QTL interval is always maximum, hence the interval with maximum observed contrast is assumed to contain the QTL. Alternative statistics for determining the interval most likely to contain a QTL are presented for contrast mapping, as well as a bootstrap estimation of the probability of having identified the correct interval. For an initial marker bracket of 20 cM and 10 additional equidistant markers, the probability of assigning the QTL to the correct 2 cM marker interval or to a combined 4 cM interval was calculated. For substitution effects of 0.093, 0.232, 0.464, 0.696 and 0.928 (in additive genetic SD), power values near 0.14, 0.26, 0.48, 0.67 and 0.80 (0.25, 0.53, 0.86, 0.97 and 0.99) are achieved for a family of 200 (1000) sons, respectively. In substitution mapping, QTL segregation status of recombinant sons must be determined using daughter genotyping. Combinations of two haplotypes with their segregation status are required to assign the QTL to an interval. Probabilities of correct QTL assignment were calculated assuming absence of the mutant QTL allele in dams of sons. For a 2 cM interval and a QTL at the midpoint of an interval, power near 0.95 (0.90) is reached when the number of recombinant sons is 70 (60), or total number of sons is 424 (363). For QTL positions away from the midpoint, power decreases but can be improved by combining marker intervals. For a QTL located halfway to the midpoint, and 182 sons in a family resulting in 30 recombinant sons, probability is 0.94 for assignment to either a 2 cM or a combined 4 cM interval. Effect of type I and type II errors in segregation status determination on power of QTL assignment was found to be small. Errors in segregation status due to QTL segregation in dams have an impact if the frequency of the mutant QTL allele is intermediate to high.     

Controlling the Type I and Type II Errors in Mapping Quantitative Trait Loci

Although the interval mapping method is widely used for mapping quantitative trait loci (QTLs), it is not very well suited for mapping multiple QTLs. Here, we present the results of a computer simulation to study the application of exact and approximate models for multiple QTLs. In particular, we focus on an automatic two-stage procedure in which in the first stage ``important' markers are selected in multiple regression on markers. In the second stage a QTL is moved along the chromosomes by using the pre-selected markers as cofactors, except for the markers flanking the interval under study. A refined procedure for cases with large numbers of marker cofactors is described. Our approach will be called MQM mapping, where MQM is an acronym for ``multiple-QTL models' as well as for ``marker-QTL-marker.' Our simulation work demonstrates the great advantage of MQM mapping compared to interval mapping in reducing the chance of a type I error (i.e., a QTL is indicated at a location where actually no QTL is present) and in reducing the chance of a type II error (i.e., a QTL is not detected).     

QTL analysis for grain protein content using SSR markers and validation studies using NILs in bread wheat

QTL interval mapping for grain protein content (GPC) in bread wheat was conducted for the first time, using a framework map based on a mapping population, which was available in the form of 100 recombinant inbred lines (RILs). The data on GPC for QTL mapping was recorded by growing the RILs in five different environments representing three wheat growing locations from Northern India; one of these locations was repeated for 3 years. Distribution of GPC values followed normal distributions in all the environments, which could be explained by significant g x e interactions observed through analyses of variances, which also gave significant effects due to genotypes and environments. Thirteen (13) QTLs were identified in individual environments following three methods (single-marker analysis or SMA, simple interval mapping or SIM and composite interval mapping or CIM) and using LOD scores that ranged from 2.5 to 6.5. Threshold LOD scores (ranging from 3.05 to 3.57), worked out and used in each case, however, detected only seven of the above 13 QTLs. Only four (QGpc.ccsu-2B.1; QGpc.ccsu-2D.1; QGpc.ccsu-3D.1 and QGpc.ccsu-7A.1) of these QTLs were identified either in more than one location or following one more method other than CIM; another QTL (QGpc.ccsu-3D.2), which was identified using means for all the environments, was also considered to be important. These five QTLs have been recommended for marker-assisted selection (MAS). The QTLs identified as above were also validated using ten NILs derived from three crosses. Five of the ten NILs possessed 38 introgressed segments from 16 chromosomes and carried 42 of the 173 markers that were mapped. All the seven QTLs were associated with one or more of the markers carried by the above introgressed segments, thus validating the corresponding markers. More markers associated with many more QTLs to be identified should become available in the future by effective MAS for GPC improvement.     

Multipoint identity-by-descent prediction using dense markers to map quantitative trait loci and estimate effective population size

A novel multipoint method, based on an approximate coalescence approach, to analyze multiple linked markers is presented. Unlike other approximate coalescence methods, it considers all markers simultaneously but only two haplotypes at a time. We demonstrate the use of this method for linkage disequilibrium (LD) mapping of QTL and estimation of effective population size. The method estimates identity-by-descent (IBD) probabilities between pairs of marker haplotypes. Both LD and combined linkage and LD mapping rely on such IBD probabilities. The method is approximate in that it considers only the information on a pair of haplotypes, whereas a full modeling of the coalescence process would simultaneously consider all haplotypes. However, full coalescence modeling is computationally feasible only for few linked markers. Using simulations of the coalescence process, the method is shown to give almost unbiased estimates of the effective population size. Compared to direct marker and haplotype association analyses, IBD-based QTL mapping showed clearly a higher power to detect a QTL and a more realistic confidence interval for its position. The modeling of LD could be extended to estimate other LD-related parameters such as recombination rates.     

A model for quantitative trait loci mapping,linkage phase,and segregation pattern estimation for a full-sib progeny

Quantitative trait loci (QTL) mapping is an important approach for the study of the genetic architecture of quantitative traits. For perennial species, inbred lines cannot be obtained due to inbreed depression and a long juvenile period. Instead, linkage mapping can be performed by using a full-sib progeny. This creates a complex scenario because both markers and QTL alleles can have different segregation patterns as well as different linkage phases between them. We present a two-step method for QTL mapping using full-sib progeny based on composite interval mapping (i.e., interval mapping with cofactors), considering an integrated genetic map with markers with different segregation patterns and conditional probabilities obtained by a multipoint approach. The model is based on three orthogonal contrasts to estimate the additive effect (one in each parent) and dominance effect. These estimatives are obtained using the EM algorithm. In the first step, the genome is scanned to detect QTL. After, segregation pattern and linkage phases between QTL and markers are estimated. A simulated example is presented to validate the methodology. In general, the new model is more effective than existing approaches, because it can reveal QTL present in a full-sib progeny that segregates in any pattern present and can also identify dominance effects. Also, the inclusion of cofactors provided more statistical power for QTL mapping.     

植物QTL定位方法的研究进展

本文系统地介绍了QTL定位的单一标记分析法、区间作图法以及复合区间作图法、混合显性模型的分析方法,概述了一些主要定位方法的分析原理、存在的主要优缺点。单一标记分析法可以采用方差分析、回归分析或似然比检验的方法分析。区间作图法和复合区间作图法是基于两个相邻标记的QTL定位方法,可采用回归分析或最大似然法分析。复合区间作图法在模型中包括了与其他QTL连锁的标记,可以提高作图的精度和效率。混合线性模型的QTL定位方法可以包括复杂的遗传效应及QTL与环境的互作效应,具有更广阔的应用前景。 Abstract:QTL mapping methods are reviewed for single-marker mapping,interval mapping,composite interval mapping,and mixed-model based method.Statistical approaches along with their properties are discussed for the mapping methods.ANOVA,regression method and likelihood ratio test can be applied in single-marker mapping.Interval mapping and composite interval mapping can be conducted,based on two interval markers,by regression method and maximum likelihood method.Since markers linked with other QTLs are include in the model,composite interval mapping is more precision and powerful.Mapping QTL by mixed-model approaches is more applicable when complicated QTL effects as well as QTL by environment interaction are analyzed.     

Joint QTL linkage mapping for multiple-cross mating design sharing one common parent

Background

Nested association mapping (NAM) is a novel genetic mating design that combines the advantages of linkage analysis and association mapping. This design provides opportunities to study the inheritance of complex traits, but also requires more advanced statistical methods. In this paper, we present the detailed algorithm of a QTL linkage mapping method suitable for genetic populations derived from NAM designs. This method is called joint inclusive composite interval mapping (JICIM). Simulations were designed on the detected QTL in a maize NAM population and an Arabidopsis NAM population so as to evaluate the efficiency of the NAM design and the JICIM method.

Principal Findings

Fifty-two QTL were identified in the maize population, explaining 89% of the phenotypic variance of days to silking, and nine QTL were identified in the Arabidopsis population, explaining 83% of the phenotypic variance of flowering time. Simulations indicated that the detection power of these identified QTL was consistently high, especially for large-effect QTL. For rare QTL having significant effects in only one family, the power of correct detection within the 5 cM support interval was around 80% for 1-day effect QTL in the maize population, and for 3-day effect QTL in the Arabidopsis population. For smaller-effect QTL, the power diminished, e.g., it was around 50% for maize QTL with an effect of 0.5 day. When QTL were linked at a distance of 5 cM, the likelihood of mapping them as two distinct QTL was about 70% in the maize population. When the linkage distance was 1 cM, they were more likely mapped as one single QTL at an intermediary position.

Conclusions

Because it takes advantage of the large genetic variation among parental lines and the large population size, NAM is a powerful multiple-cross design for complex trait dissection. JICIM is an efficient and specialty method for the joint QTL linkage mapping of genetic populations derived from the NAM design.     

A comparison of interval mapping procedures including the QTL-cluster mapping

Using the deterministic sampling, patterns of the log-likelihood surfaces expected in some interval mapping procedures for estimating the position of, and the effect for, QTL(s) were investigated for the situations where a single QTL or closely linked QTLs are contained in a chromosome segment bracketed with two markers. The mapping procedures compared were the conventional, likelihood-based interval mapping (IM), the regression interval mapping (RIM), and the QTL-cluster mapping (CM) in which the conditional probabilities of transmission of the whole segment marked by the flanking markers were taken into consideration. The half-sib design was used here, and several cases of the true genetic model were considered, differing in the number of QTLs contained in the marker interval, the linkage phase for the sire, and the magnitude of the QTL(s) effect. For the true genetic models where a single QTL or closely linked QTLs being in coupling phase are contained in the interval, with (R)IM, clear global maxima of the log-likelihood were observed within the range of the marker interval. It was shown that the estimates of the QTL(s) effect at the marked segment level are expected to be unbiased. On the other hand, in a setting where the linkage phase for the linked QTLs located in the interval was different from coupling and repulsion, there was found a ridge along the interval for the log-likelihood surface with (R)IM, indicating the dependency between the estimates of the position of, and the effect for, the putative QTL. For this case, it was found that the position of the putative QTL could be estimated as that of one of the flanking markers, and the estimate of the QTL effect be biased. In contrast, it was revealed that CM is expected to provide the unbiased estimate of the QTL(s)-effect at the segment level for any case of the true genetic models considered here. If the aim is for marker-assisted selection rather than mapping closely linked QTLs individually, then the CM approach is considered to be useful.     

On the differences between maximum likelihood and regression interval mapping in the analysis of quantitative trait loci

The differences between maximum-likelihood (ML) and regression (REG) interval mapping in the analysis of quantitative trait loci (QTL) are investigated analytically and numerically by simulation. The analytical investigation is based on the comparison of the solution sets of the ML and REG methods in the estimation of QTL parameters. Their differences are found to relate to the similarity between the conditional posterior and conditional probabilities of QTL genotypes and depend on several factors, such as the proportion of variance explained by QTL, relative QTL position in an interval, interval size, difference between the sizes of QTL, epistasis, and linkage between QTL. The differences in mean squared error (MSE) of the estimates, likelihood-ratio test (LRT) statistics in testing parameters, and power of QTL detection between the two methods become larger as (1) the proportion of variance explained by QTL becomes higher, (2) the QTL locations are positioned toward the middle of intervals, (3) the QTL are located in wider marker intervals, (4) epistasis between QTL is stronger, (5) the difference between QTL effects becomes larger, and (6) the positions of QTL get closer in QTL mapping. The REG method is biased in the estimation of the proportion of variance explained by QTL, and it may have a serious problem in detecting closely linked QTL when compared to the ML method. In general, the differences between the two methods may be minor, but can be significant when QTL interact or are closely linked. The ML method tends to be more powerful and to give estimates with smaller MSEs and larger LRT statistics. This implies that ML interval mapping can be more accurate, precise, and powerful than REG interval mapping. The REG method is faster in computation, especially when the number of QTL considered in the model is large. Recognizing the factors affecting the differences between REG and ML interval mapping can help an efficient strategy, using both methods in QTL mapping to be outlined.     

Evaluation of genome-wide selection efficiency in maize nested association mapping populations

In comparison to conventional marker-assisted selection (MAS), which utilizes only a subset of genetic markers associated with a trait to predict breeding values (BVs), genome-wide selection (GWS) improves prediction accuracies by incorporating all markers into a model simultaneously. This strategy avoids risks of missing quantitative trait loci (QTL) with small effects. Here, we evaluated the accuracy of prediction for three corn flowering traits days to silking, days to anthesis, and anthesis-silking interval with GWS based on cross-validation experiments using a large data set of 25 nested association mapping populations in maize (Zea mays). We found that GWS via ridge regression-best linear unbiased prediction (RR-BLUP) gave significantly higher predictions compared to MAS utilizing composite interval mapping (CIM). The CIM method may be selected over multiple linear regression to decrease over-estimations of the efficiency of GWS over a MAS strategy. The RR-BLUP method was the preferred method for estimating marker effects in GWS with prediction accuracies comparable to or greater than BayesA and BayesB. The accuracy with RR-BLUP increased with training sample proportion, marker density, and heritability until it reached a plateau. In general, gains in accuracy with RR-BLUP over CIM increased with decreases of these factors. Compared to training sample proportion, the accuracy of prediction with RR-BLUP was relatively insensitive to marker density.     

Approximate Thresholds of Interval Mapping Tests for Qtl Detection

A general method is proposed for calculating approximate thresholds of interval mapping tests for quantitative trait loci (QTL) detection. Simulation results show that this method, when applied to backcross and F(2) populations, gives good approximations and is useful for any situation. Programs which calculate these thresholds for backcross, recombinant inbreds and F(2) for any given level and any chromosome with any given distribution of codominant markers were written in Fortran 77 and are available under request. The approach presented here could be used to obtain, after suitable calculations, thresholds for most segregating populations used in QTL mapping experiments.     

Single- and multiple-trait mapping analysis of linked quantitative trait loci. Some asymptotic analytical approximations.

Estimating the resolution power of mapping analysis of linked quantitative trait loci (QTL) remains a difficult problem, which has been previously addressed mainly by Monte Carlo simulations. The analytical method of evaluation of the expected LOD developed in this article spreads the "deterministic sampling" approach for the case of two linked QTL for single- and two-trait analysis. Several complicated questions are addressed through this evaluation: the dependence of QTL detection power on the QTL effects, residual correlation between the traits, and the effect of epistatic interaction between the QTL for one or both traits on expected LOD (ELOD), etc. Although this method gives only an asymptotic estimation of ELOD, it allows one to get an approximate assessment of a broad spectrum of mapping situations. A good correspondence was found between the ELODs predicted by the model and LOD values averaged over Monte Carlo simulations.     

Morphological traits defining species differences in wild relatives of maize are controlled by multiple quantitative trait loci

We analyzed the genetic basis of morphological differences between two wild species of teosinte (Zea diploperennis and Z. mays ssp. parviglumis), which are relatives of maize. These two species differ in a number of taxonomically important traits including the structure of the tassel (male inflorescence), which is the focus of this report. To investigate the genetic inheritance of six tassel traits, quantitative trait locus (QTL) mapping with 95 RFLP markers was employed on a population of 425 F2 plants. Each trait was analyzed by interval mapping (IM) and composite interval mapping (CIM) to identify and characterize the QTL controlling the differences in tassel morphology. We detected two to eight QTL for each trait. In total, 30 QTL with IM and 33 QTL with CIM were found for tassel morphology. QTL for several of the traits mapped near each other, suggesting pleiotropy and/or linkage of QTL. The QTL showed small to moderate magnitudes of effect. No QTL of exceptionally large effect were found as seen under domestication and in the case of some other natural species. Thus, the model involving major QTL of large effect seems not to apply to the traits and species analyzed. A mixture of QTL with positive and negative allelic effects was found for most tassel traits and may suggest a history of periodic changes in the direction of selection during the divergence of Z. diploperennis and Z. mays ssp. parviglumis or fixation of QTL alleles by random genetic drift rather than selection.     

Mapping the epistatic network underlying murine reproductive fatpad variation

Genome-wide mapping analyses are now commonplace in many species and several networks of interacting loci have been reported. However, relatively few details regarding epistatic interactions and their contribution to complex trait variation in multicellular organisms are available and the identification of positional candidate loci for epistatic QTL (epiQTL) is hampered, especially in mammals, by the limited genetic resolution inherent in most study designs. Here we further investigate the genetic architecture of reproductive fatpad weight in mice using the F(10) generation of the LG,SM advanced intercross (AI) line. We apply multiple mapping techniques including a single-locus model, locus-specific composite interval mapping (CIM), and tests for multiple QTL per chromosome to the 12 chromosomes known to harbor single-locus QTL (slQTL) affecting obesity in this cross. We also perform a genome-wide scan for pairwise epistasis. Using this combination of approaches we detect 199 peaks spread over all 19 autosomes, which potentially contribute to trait variation including all eight original F(2) loci (Adip1-8), novel slQTL peaks on chromosomes 7 and 9, and several novel epistatic loci. Extensive epistasis is confirmed involving both slQTL confidence intervals (C.I.) as well as regions that show no significant additive or dominance effects. These results provide important new insights into mapping complex genetic architectures and the role of epistasis in complex trait variation.