Adjusting for covariates in variance components QTL linkage analysis.

BackgroundWe analyzed the Genetic Analysis Workshop 13 (GAW13) simulated data to contrast and compare different methods for the genetic linkage analysis of hypertension and change in blood pressure over time. We also examined methods for incorporating covariates into the linkage analysis. We used methods for quantitative trait loci (QTL) linkage analysis with and without covariates and affected sib-pair (ASP) analysis of hypertension followed by ordered subset analysis (OSA), using variables associated with change in blood pressure over time.ResultsFour of the five baseline genes and one of the three slope genes were not detected by any method using conventional criteria. OSA detected baseline gene b35 on chromosome 13 when using the slope in blood pressure to adjust for change over time. Slope gene s10 was detected by the ASP analysis and slope gene s11 was detected by QTL linkage analysis as well as by OSA analysis. Analysis of null chromosomes, i.e., chromosomes without genes, did not reveal significant increases in type I error. However, there were a number of genes indirectly related to blood pressure detected by a variety of methods.ConclusionsWe noted that there is no obvious first choice of analysis software for analyzing a complicated model, such as the one underlying the GAW13 simulated data. Inclusion of covariates and longitudinal data can improve localization of genes for complex traits but it is not always clear how best to do this. It remains a worthwhile task to apply several different approaches since one method is not always the best.

BackgroundVariance component (VC) models are commonly used for Quantitative Trait Loci (QTL) mapping in outbred populations. Here, the QTL effect is given as a random effect and a critical part of the model is the relationship between the phenotypic values and the random effect. In the traditional VC model, each individual has a unique QTL effect and the relationship between these random effects is given as a covariance structure (known as the identity-by-descent (IBD) matrix).ResultsWe present an alternative notation of the variance component model, where the elements of the random effect are independent base generation allele effects and sampling term effects. The relationship between the phenotypic vales and the random effect is given by an incidence matrix, which results in a novel, but statistically equivalent, version of the traditional VC model. A general algorithm to estimate this incidence matrix is presented. Since the model is given in terms of base generation allele effects and sampling term effects, these effects can be estimated separately using best linear unbiased prediction (BLUP). From simulated data, we showed that biallelic QTL effects could be accurately clustered using the BLUP obtained from our model notation when markers are fully informative, and that the accuracy increased with the size of the QTL effect. We also developed a measure indicating whether a base generation marker homozygote is a QTL heterozygote or not, by comparing the variances of the sampling term BLUP and the base generation allele BLUP. A ratio greater than one gives strong support for a QTL heterozygote.ConclusionWe developed a simple presentation of the VC QTL model for identification of base generation allele effects in QTL linkage analysis. The base generation allele effects and sampling term effects were separated in our model notation. This clarifies the assumptions of the model and should also enhance the development of genome scan methods.

To increase the likelihood of finding genetic variation conferring liability to eating disorders, we measured over 100 attributes thought to be related to liability to eating disorders on affected individuals from multiplex families and two cohorts: one recruited through a proband with anorexia nervosa (AN; AN cohort); the other recruited through a proband with bulimia nervosa (BN; BN cohort). By a multilayer decision process based on expert evaluation and statistical analysis, six traits were selected for linkage analysis (1): obsessionality (OBS), age at menarche (MENAR), and anxiety (ANX) for quantitative trait locus (QTL) linkage analysis; and lifetime minimum body mass index (BMI), concern over mistakes (CM), and food-related obsessions (OBF) for covariate-based linkage analysis. The BN cohort produced the largest linkage signals: for QTL linkage analysis, four suggestive signals: (for MENAR, at 10p13; for ANX, at 1q31.1, 4q35.2, and 8q13.1); for covariate-based linkage analyses, both significant and suggestive linkages (for BMI, one significant [4q21.1] and three suggestive [3p23, 10p13, 5p15.3]; for CM, two significant [16p13.3, 14q21.1] and three suggestive [4p15.33, 8q11.23, 10p11.21]; and for OBF, one significant [14q21.1] and five suggestive [4p16.1, 10p13.1, 8q11.23, 16p13.3, 18p11.31]). Results from the AN cohort were far less compelling: for QTL linkage analysis, two suggestive signals (for OBS at 6q21 and for ANX at 9p21.3); for covariate-based linkage analysis, five suggestive signals (for BMI at 4q13.1, for CM at 11p11.2 and 17q25.1, and for OBF at 17q25.1 and 15q26.2). Overlap between the two cohorts was minimal for substantial linkage signals.

The common assumption in quantitative trait locus (QTL) linkage mapping studies that parents of multiple connected populations are unrelated is unrealistic for many plant breeding programs. We remove this assumption and propose a Bayesian approach that clusters the alleles of the parents of the current mapping populations from locus-specific identity by descent (IBD) matrices that capture ancestral marker and pedigree information. Moreover, we demonstrate how the parental IBD data can be incorporated into a QTL linkage analysis framework by using two approaches: a Threshold IBD model (TIBD) and a Latent Ancestral Allele Model (LAAM). The TIBD and LAAM models are empirically tested via numerical simulation based on the structure of a commercial maize breeding program. The simulations included a pilot dataset with closely linked QTL on a single linkage group and 100 replicated datasets with five linkage groups harboring four unlinked QTL. The simulation results show that including parental IBD data (similarly for TIBD and LAAM) significantly improves the power and particularly accuracy of QTL mapping, e.g., position, effect size and individuals’ genotype probability without significantly increasing computational demand.

Gene-environment interaction (G x E) is likely to be a common and important source of variation for complex behavioral traits. Gene-environment interaction, or genetic control of sensitivity to the environment, can be incorporated into variance components twin and sib-pair analyses by partitioning genetic effects into a mean part, which is independent of the environment, and a part that is a linear function of the environment. An approach described in a companion paper (Purcell, 2002) is applied to sib-pair variance components linkage analysis in two ways: allowing for quantitative trait locus by environment interaction and utilizing information on any residual interactions detected prior to analysis. As well as elucidating environmental pathways, consideration of G x E in quantitative and molecular studies will potentially direct and enhance gene-mapping efforts.

Both lipoprotein-associated phospholipase A(2) (Lp-PLA(2)) activity, a biomarker of inflammation, and concentration of its primary associated lipoprotein, LDL, are correlated with adverse coronary outcomes. We previously reported a quantitative trait locus (QTL) corresponding to HSA2p24.3-p23.2 with pleiotropic effects on Lp-PLA(2) activity and LDL-cholesterol (LDL-C) concentration in baboons fed a basal diet. Here, our goal was to locate pleiotropic QTLs influencing both traits in the same baboons fed a high-cholesterol, high-fat (HCHF) diet, and to assess whether shared genetic effects on these traits differ between diets. We assayed Lp-PLA(2) activity and LDL-C concentration in 683 baboons fed the HCHF diet. We used a bivariate maximum likelihood-based variance components approach in whole-genome linkage screens to locate a QTL [logarithm of odds (LOD) = 3.13, genome-wide P = 0.019] corresponding to HSA19q12-q13.2 with pleiotropic effects on Lp-PLA(2) activity and LDL-C levels in the HCHF diet. We additionally found significant evidence of genetic variance in response to diet for Lp-PLA(2) activity (P = 0.0017) and for LDL-C concentration (P = 0.00001), revealing a contribution of genotype-by-diet interaction to covariation in these two traits. We conclude that the pleiotropic QTLs detected at 2p24.3-p23.2 and 19q12-q13.2 on the basal and HCHF diets, respectively, exert diet-specific effects on covariation in Lp-PLA(2) activity and LDL-C concentration.

Due to the universal presence of genotype by environment interactions, understanding the pattern of quantitative trait loci (QTL)-by-environment interactions is a prerequisite for effective marker-assisted selection. In this report, we describe a biplot approach for investigating QTL-by-environment patterns. This approach involves two steps. It starts with a two-way table containing effects of individual QTLs in individual environments for the trait under investigation. This table is decomposed into principal components via singular value decomposition, and the first two principal components are plotted for both QTLs and environments to form a biplot. The resulting ‘QQE biplot’ contains information on QTL main effects (Q) and QTL-by-environment interactions (QE). A QQE biplot displays the QTL-by-environment patterns and allows visualization of: (1) the magnitude of the effect of a QTL, (2) the average effect of a QTL and its stability across environments, (3) the effects of a QTL in individual environments, (4) the similarity/dissimilarity among QTLs in effect and response to the environments, (5) the similarity/dissimilarity among environments in modulating QTL effects, (6) any differentiation of mega-environments, and (7) the combination of QTL alleles for maximum/minimum expression of the trait for each environment or mega-environment. A case study is provided using the QTL-by-environment two-way table for barley yield.

In a previous study on maize (Zea mays, L.) several quantitative trait loci (QTL) showing high dominance-additive ratio for agronomic traits were identified in a population of recombinant inbred lines derived from B73 × H99. For four of these mapped QTL, namely 3.05, 4.10, 7.03 and 10.03 according to their chromosome and bin position, families of near-isogenic lines (NILs) were developed, i.e., couples of homozygous lines nearly identical except for the QTL region that is homozygote either for the allele provided by B73 or by H99. For two of these QTL (3.05 and 4.10) the NILs families were produced in two different genetic backgrounds. The present research was conducted in order to: (i) characterize these QTL by estimating additive and dominance effects; (ii) investigate if these effects can be affected by genetic background, inbreeding level and environmental growing conditions (low vs. high plant density). The six NILs’ families were tested across three years and in three Experiments at different inbreeding levels as NILs per se and their reciprocal crosses (Experiment 1), NILs crossed to related inbreds B73 and H99 (Experiment 2) and NILs crossed to four unrelated inbreds (Experiment 3). Experiment 2 was conducted at two plant densities (4.5 and 9.0 plants m-2). Results of Experiments 1 and 2 confirmed previous findings as to QTL effects, with dominance-additive ratio superior to 1 for several traits, especially for grain yield per plant and its component traits; as a tendency, dominance effects were more pronounced in Experiment 1. The QTL effects were also confirmed in Experiment 3. The interactions involving QTL effects, families and plant density were generally negligible, suggesting a certain stability of the QTL. Results emphasize the importance of dominance effects for these QTL, suggesting that they might deserve further studies, using NILs’ families and their crosses as base materials.

Traditional methods for detection and mapping of quantitative trait loci (QTL) in dairy cattle populations are usually based on daughter design (DD) or granddaughter design (GDD). Although these designs are well established and usually successful in detecting QTL, they consider sire families independently of each other, thereby ignoring relationships among other animals in the population and consequently, reducing the power of QTL detection. In this study we compared a traditional GDD with a general pedigree design (GPD) and assessed the precision and power of both methods for detecting and locating QTL in a simulated complex pedigree. QTL analyses were performed under the variance component model containing a random QTL and a random polygenic effect. The covariance matrix of the polygenic effect was a standard additive relationship matrix. The (co)variance matrix of the random QTL effect contained probabilities that QTL alleles shared by two individuals were identical by descent (IBD). In the GDD analysis, IBD probabilities were calculated using sires’ and daughters’ marker genotypes. In the GPD analysis, IBD probabilities were obtained using a deterministic approach. The estimation of QTL position and variance components was conducted using REML algorithm. Although both methods were able to locate the region of the QTL properly, the GPD method showed better precision of QTL position estimates in most cases and significantly higher power than the GDD method.  

Using a novel approach for a two-locus model that provides a greatly increased power to detect multiple quantitative trait loci (QTLs) in simulated data, we identified in a sample of 961 female sib-pairs, three genome-wide significant QTLs for apolipoprotein A1 on chromosomes 8p21.1-q13.1 (LOD score 3.71), 9q21.32-33.1 (LOD score 3.28) and 10p15.1-p13 (LOD score 5.51), two for lipoprotein (a) on chromosomes 6q25.2-q27 (LOD score 10.18) and 21q21.1-q21.3 (LOD score 4.57) and two for triglycerides on chromosomes 4q28.3-32.1 (LOD score 3.71) and 5q23.1-q32 (LOD score 3.60). The two-locus ordered-subset analysis has led to the confirmation of known and likely identification of novel regions linked to serum lipid levels that would have otherwise been missed and deserves wider application in linkage analyses of quantitative traits. Given the relative lack of power for the sample sizes commonly used in human genetics linkage studies, minor QTL effects often go undetected and those that are detected will be upwardly biased. We show through simulation that the discrepancy between the real and estimated QTL-effects is often likely to generate an unpredictable source of false-negative errors, using multi-locus models, reducing the power to detect multiple QTLs through oligogenic linkage analysis. The successful simultaneous modelling of the identified QTLs in a multi-locus context helps to eliminate false positives and increases the power to detect linkages, adding compelling evidence that they are likely to be reliable QTLs for these lipid traits.

Key messageUsing QTL analysis and fine mapping, the novel recessive gene xa44(t) conferring resistance to BB was identified and the expression level of the gene was confirmed through qRT-PCR analysis.Bacterial blight (BB) disease caused by Xanthomonas oryzae pv. oryzae (Xoo) is a major factor causing rice yield loss in most rice-cultivating countries, especially in Asia. The deployment of cultivars with resistance to BB is the most effective method to control the disease. However, the evolution of new Xoo or pathotypes altered by single-gene-dependent mutations often results in breakdown of resistance. Thus, efforts to identify novel R-genes with sustainable BB resistance are urgently needed. In this study, we identified three quantitative trait loci (QTLs) on chromosomes 1, 4, and 11, from an F2 population of 493 individuals derived from a cross between IR73571-3B-11-3-K3 and Ilpum using a 7K SNP chip. Of these QTLs, one major QTL, qBB_11, on chromosome 11 explained 61.58% of the total phenotypic variance in the population, with an LOD value of 113.59, based on SNPs 11964077 and 11985463. The single major R-gene, with recessive gene action, was designated xa44(t) and was narrowed down to a 120-kb segment flanked within 28.00 Mbp to 28.12 Mbp. Of nine ORFs present in the target region, two ORFs revealed significantly different expression levels of the candidate genes. These candidate genes (Os11g0690066 and Os11g0690466) are described as “serine/threonine protein kinase domain containing protein” and “hypothetical protein,” respectively. The results will be useful to further understand BB resistance mechanisms and provide new sources of resistance, together with DNA markers for MAS breeding to improve BB resistance in rice.

Key messageA comprehensive linkage atlas for seed yield in rapeseed.Most agronomic traits of interest for crop improvement (including seed yield) are highly complex quantitative traits controlled by numerous genetic loci, which brings challenges for comprehensively capturing associated markers/genes. We propose that multiple trait interactions underlie complex traits such as seed yield, and that considering these component traits and their interactions can dissect individual quantitative trait loci (QTL) effects more effectively and improve yield predictions. Using a segregating rapeseed (Brassica napus) population, we analyzed a large set of trait data generated in 19 independent experiments to investigate correlations between seed yield and other complex traits, and further identified QTL in this population with a SNP-based genetic bin map. A total of 1904 consensus QTL accounting for 22 traits, including 80 QTL directly affecting seed yield, were anchored to the B. napus reference sequence. Through trait association analysis and QTL meta-analysis, we identified a total of 525 indivisible QTL that either directly or indirectly contributed to seed yield, of which 295 QTL were detected across multiple environments. A majority (81.5%) of the 525 QTL were pleiotropic. By considering associations between traits, we identified 25 yield-related QTL previously ignored due to contrasting genetic effects, as well as 31 QTL with minor complementary effects. Implementation of the 525 QTL in genomic prediction models improved seed yield prediction accuracy. Dissecting the genetic and phenotypic interrelationships underlying complex quantitative traits using this method will provide valuable insights for genomics-based crop improvement.

Strategies for mapping quantitative trait (QT) loci (QTL) need special studies in species where inbred lines cannot easily be obtained. We propose the detection of QTL with schemes including several full-sib families and study here the efficiency of such schemes — diallel, factorial, cyclic or single-pair mating designs — for a varying number of parents, on the basis of the power of tests for QTL detection. We considered a single, fully informative marker completely linked to the QTL. The tests proposed for QTL detection are based on linear additive models. The results show that for a given number of parents, in given conditions, the mating design has no effect on power. It increases as the total number of individuals genotyped increases. The number of parents affects the power of QTL detection insofar as the effect of substituting QT alleles varies from one parent to another and as some parents can be homozygous at the QTL: with only two parents QTL detection is often less powerful than with more parents. The size of the genetic effects that can be detected with a given power decreases as the number of parents increases from two to six and is quite stable for more than six parents. If QTL heterozygote frequency in the base population is high enough (greater than 0.2) mating designs with six parents should give a good sample of variance attributable to QTL and allow the detection of QTL with reasonable power.

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.

Restriction fragment length polymorphisms have become powerful tools for genetic investigations in plant species. They allow a much greater degree of genome saturation with neutral markers than has been possible with isozymes or morphological loci. A previous investigation employed isozymes as genetic markers to infer the location of genetic factors influencing the expression of quantitative traits in the maize population: (CO159×Tx303)F2. This investigation was conducted to examine the inferences that might be derived using a highly saturated map of RFLP markers and isozymes to detect quantitative trait loci (QTLs) in the same maize F2 population. Marker loci that were associated with QTL effects in this investigation generally corresponded well with previous information where such comparisons were possible. Additionally, a number of previously unmarked genomic regions were found to contain factors with large effects on some plant traits. Availability of numerous marker loci in some genomic regions allowed: more accurate localization of QTLs, resolution of linkage between QTLs affecting the same traits, and determination that some chromsome regions previously found to affect a number of traits are likely to be due to linkage of QTLs affecting different traits. Many of the factors that affected plant height quantitatively in this investigation were found to map to regions also including known sites of major genes influencing plant height. Although the data are not conclusive, they suggest that some of the identified QTLs may be allelic to known major genes affecting plant height.