QTL mapping for yield and yield contributing traits in two mapping populations of bread wheat

In bread wheat, single-locus and two-locus QTL analyses were conducted for seven yield and yield contributing traits using two different mapping populations (P I and P II). Single-locus QTL analyses involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated yield traits to detect the pleiotropic QTLs. Two-locus analyses were conducted to detect main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions (QE and QQE). Only a solitary QTL for spikelets per spike was common between the above two populations. HomoeoQTLs were also detected, suggesting the presence of triplicate QTLs in bread wheat. Relatively fewer QTLs were detected in P I than in P II. This may be partly due to low density of marker loci on P I framework map (173) than in P II (521) and partly due to more divergent parents used for developing P II. Six QTLs were important which were pleiotropic/coincident involving more than one trait and were also consistent over environments. These QTLs could be utilized efficiently for marker assisted selection (MAS).     

Derivation of a Bayes factor to distinguish between linked or pleiotropic quantitative trait loci

A simple procedure to calculate the Bayes factor between linked and pleiotropic QTL models is presented. The Bayes factor is calculated from the marginal prior and posterior densities of the locations of the QTL under a linkage and a pleiotropy model. The procedure is computed with a Gibbs sampler, and it can be easily applied to any model including the location of the QTL as a variable. The procedure was compared with a multivariate least-squares method. The proposed procedure showed better results in terms of power of detection of linkage when low information is available. As information increases, the performance of both procedures becomes similar. An example using data provided by an Iberian by Landrace pig intercross is presented. The results showed that three different QTL segregate in SSC6: a pleiotropic QTL affects myristic, palmitic, and eicosadienoic fatty acids; another pleiotropic QTL affects palmitoleic, stearic, and vaccenic fatty acids; and a third QTL affects the percentage of linoleic acid. In the example, the Bayes factor approach was more powerful than the multivariate least-squares approach.     

Multitrait fine mapping of quantitative trait loci using combined linkage disequilibria and linkage analysis

A novel multitrait fine-mapping method is presented. The method is implemented by a model that treats QTL effects as random variables. The covariance matrix of allelic effects is proportional to the IBD matrix, where each element is the probability that a pair of alleles is identical by descent, given marker information and QTL position. These probabilities are calculated on the basis of similarities of marker haplotypes of individuals of the first generation of genotyped individuals, using "gene dropping" (linkage disequilibrium) and transmission of markers from genotyped parents to genotyped offspring (linkage). A small simulation study based on a granddaughter design was carried out to illustrate that the method provides accurate estimates of QTL position. Results from the simulation also indicate that it is possible to distinguish between a model postulating one pleiotropic QTL affecting two traits vs. one postulating two closely linked loci, each affecting one of the traits.     

Mapping of QTLs governing agronomic and yield traits in chickpea

Chickpea is one of the most important leguminous cool season food crops, cultivated prevalently in South Asia and Middle East. The main objective of this study was to identify quantitative trait loci (QTLs) associated with seven agronomic and yield traits in two recombinant inbred line populations of chickpea derived from the crosses JG62 × Vijay (JV population) and Vijay × ICC4958 (VI population) from at least three environments. Single locus QTL analysis involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated traits to detect pleiotropic QTLs. Two-locus analysis was conducted to identify the main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions. Through CIM analysis, a total of 106 significant QTLs (41 in JV and 65 in VI populations) were identified for the seven traits, of which one QTL each for plant height and days to maturity was common in both the populations. Six pleiotropic QTLs that were consistent over the environments were also identified. LG2 in JV and LG1a in VI contained at least one QTL for each trait. Hence, concentrating on these LGs in molecular breeding programs is most likely to bring simultaneous improvement in these traits.     

Phenotypic Integration Without Modularity: Testing Hypotheses About the Distribution of Pleiotropic Quantitative Trait Loci in a Continuous Space

Theories of phenotypic integration have relied heavily on the concept of modularity in order to model the ways in which traits in an organism correlate and covary. Recent investigations suggest that, while some functional and developmental processes may be morphologically and ontogenetically localized, and thus modular in a developmental sense, there is a great deal of overlap among these influences on patterns of integration in the adult form. This can result in blurry boundaries between hypothesized modules constructed to test hypotheses about phenotypic integration. This investigation tests hypotheses about the contribution of pleiotropic quantitative trait loci (QTL) to phenotypic integration in the mouse mandible without using a priori categorical hypotheses about which traits constitute a module. We ask two main questions: (1) Are the effects of pleiotropic QTL localized to highly correlated traits or more spread out among traits than one might expect by chance? (2) Does the pattern of trait influence when all pleiotropic QTL are considered together deviate from what we might expect if QTL affect traits without regard for the correlations among traits? We find that a large proportion of pleiotropic QTL affect traits that are more highly correlated than we expect by chance with the remainder having effects that are distributed as if by chance. Furthermore, the overall distribution of the effects of pleiotropic QTL differs significantly from the null distribution of no association between pleiotropic effects on traits and correlations among traits. The main modular hypothesis used by earlier studies often does not predict the distribution of sets of traits sharing a common QTL. These results suggest that there is a clear tendency for pleiotropic effects of QTL to be localized but that the localization may be best thought of as occurring in a continuous space rather being clustered in discrete modules.     

QTL identification of grain protein concentration and its genetic correlation with starch concentration and grain weight using two populations in maize (<Emphasis Type="Italic">Zea mays</Emphasis> L.)

Protein is one of the three main storage chemical components in maize grains, and is negatively correlated with starch concentration (SC). Our objective was to analyse the influence of genetic backgrounds on QTL detection for protein concentration (PC) and to reveal the molecular genetic associations between PC and both SC and grain weight (GWP). Two hundred and eighty-four (Pop1) and 265 (Pop2) F_2:3 families were developed from two crosses between one high-oil maize inbred GY220 and two normal maize inbreds 8984 and 8622 respectively, and were genotyped with 185 and 173 pairs of SSR markers. PC, SC and GWP were evaluated under two environments. Composite interval mapping (CIM) and multiple interval mapping (MIM) methods were used to detect single-trait QTL for PC, and multiple-trait QTL for PC with both SC and GWP. No common QTL were shared between the two populations for their four and one PC QTL. Common QTL with opposite signs of effects for PC and SC/GWP were detected on three marker intervals at bins 6.07–6.08, 8.03 and 8.03–8.04. Multiple-traits QTL mapping showed that tightly-linked QTL, pleiotropic QTL and QTL having effects with opposite directions for PC and SC/GWP were all observed in Pop1, while all QTL reflected opposite effects in Pop2.     

Multiple Trait Analysis of Genetic Mapping for Quantitative Trait Loci

We present in this paper models and statistical methods for performing multiple trait analysis on mapping quantitative trait loci (QTL) based on the composite interval mapping method. By taking into account the correlated structure of multiple traits, this joint analysis has several advantages, compared with separate analyses, for mapping QTL, including the expected improvement on the statistical power of the test for QTL and on the precision of parameter estimation. Also this joint analysis provides formal procedures to test a number of biologically interesting hypotheses concerning the nature of genetic correlations between different traits. Among the testing procedures considered are those for joint mapping, pleiotropy, QTL by environment interaction, and pleiotropy vs. close linkage. The test of pleiotropy (one pleiotropic QTL at a genome position) vs. close linkage (multiple nearby nonpleiotropic QTL) can have important implications for our understanding of the nature of genetic correlations between different traits in certain regions of a genome and also for practical applications in animal and plant breeding because one of the major goals in breeding is to break unfavorable linkage. Results of extensive simulation studies are presented to illustrate various properties of the analyses.     

Segregation of QTL for production traits in commercial meat-type chickens

This study investigated whether quantitative trait loci (QTL) identified in experimental crosses of chickens provide a short cut to the identification of QTL in commercial populations. A commercial population of broilers was targeted for chromosomal regions in which QTL for traits associated with meat production have previously been detected in extreme crosses. A three-generation design, consisting of 15 grandsires, 608 half-sib hens and over 15 000 third-generation offspring, was implemented within the existing breeding scheme of a broiler breeding company. The first two generations were typed for 52 microsatellite markers spanning regions of nine chicken chromosomes and covering a total of 730 cM, approximately one-fifth of the chicken genome. Using half-sib analyses with a multiple QTL model, linkage was studied between these regions and 17 growth and carcass traits. Out of 153 trait x region comparisons, 53 QTL exceeded the threshold for genome-wide significance while an additional 23 QTL were significant at the nominal 1% level. Many of the QTL affect the carcass proportions and feed intake, for which there are few published studies. Given intensive selection for efficient growth in broilers for more than 50 generations it is surprising that many QTL affecting these traits are still segregating. Future fine-mapping efforts could elucidate whether ancestral mutations are still segregating as a result of pleiotropic effects on fitness traits or whether this variation is due to new mutations.     

Time-related mapping of quantitative trait loci underlying tiller number in rice.

Using time-related phenotypic data, methods of composite interval mapping and multiple-trait composite interval mapping based on least squares were applied to map quantitative trait loci (QTL) underlying the development of tiller number in rice. A recombinant inbred population and a corresponding saturated molecular marker linkage map were constructed for the study. Tiller number was recorded every 4 or 5 days for a total of seven times starting at 20 days after sowing. Five QTL were detected on chromosomes 1, 3, and 5. These QTL explained more than half of the genetic variance at the final observation. All the QTL displayed an S-shaped expression curve. Three QTL reached their highest expression rates during active tillering stage, while the other two QTL achieved this either before or after the active tillering stage.     

Marker assisted selection for the improvement of two antagonistic traits under mixed inheritance

A Monte Carlo simulation was used to investigate the potential of Marker Assisted Selection (MAS) in a multiple-trait situation. Only additive effects were considered. The base population was assumed to be in linkage equilibrium and, next, the population was managed over 15 discrete generations, 10 males and 50 females were chosen out of the 100 candidates of each sex. Performance for two traits was simulated with an overall heritability of a given trait equal to 0.25 or 0.10 and the overall genetic correlation between traits was generally equal to -0.4 except in one case where it was equal to 0. The model involved one biallelic QTL, accounting for 10 or 20% of the genetic variance of a given trait, plus polygenes. Initial allelic frequencies at the QTL were generally equal to 0.5 but in one case were equal to 0.1 and 0.9. A marker with 120 different alleles in the 60 founder parents was simulated in the vicinity of the QTL. Two values of the recombination rate between these two loci were considered, 0.10 and 0.02. The genetic evaluation was based on a multiple-trait BLUP animal model, accounting (MAS) or not (conventional BLUP) for marker information. Two sets of simulations were run: (1) a "missing data"case, with males having no record for one of the traits, and (2) a "secondary trait"case, with one trait having a weight in the aggregate genotype 4 times less than the other trait and the QTL acting only on this secondary trait. In the first set, evaluation methods were found to mainly affect the accuracy of overall genetic values prediction for the trait with missing data. In comparison with BLUP, MAS led to an extra overall genetic response for the trait with missing data, which was strongly penalised under the conventional BLUP, and to a deficit in response for the other trait. This more balanced evolution of the two traits was obtained, however, at the expense of the long-term overall cumulated response for the aggregate genotype, which was 1 to 2.5% lower than the one obtained under the conventional BLUP. In the second set of simulation, in the case of low initial frequency (0.1) of the QTL allele favourable to the secondary trait, MAS was found to be substantially more efficient to avoid losing this allele than BLUP only when the QTL had a large effect and the marker was close. More benefits should be expected from MAS with more specific applications, such as early selection of animals, or by applying dynamic procedures i.e. letting the respective weights to QTL and polygenic values in the selection criterion vary across generation.     

Do large effect QTL fractionate? A case study at the maize domestication QTL teosinte branched1

Quantitative trait loci (QTL) mapping is a valuable tool for studying the genetic architecture of trait variation. Despite the large number of QTL studies reported in the literature, the identified QTL are rarely mapped to the underlying genes and it is usually unclear whether a QTL corresponds to one or multiple linked genes. Similarly, when QTL for several traits colocalize, it is usually unclear whether this is due to the pleiotropic action of a single gene or multiple linked genes, each affecting one trait. The domestication gene teosinte branched1 (tb1) was previously identified as a major domestication QTL with large effects on the differences in plant and ear architecture between maize and teosinte. Here we present the results of two experiments that were performed to determine whether the single gene tb1 explains all trait variation for its genomic region or whether the domestication QTL at tb1 fractionates into multiple linked QTL. For traits measuring plant architecture, we detected only one QTL per trait and these QTL all mapped to tb1. These results indicate that tb1 is the sole gene for plant architecture traits that segregates in our QTL mapping populations. For most traits related to ear morphology, we detected multiple QTL per trait in the tb1 genomic region, including a large effect QTL at tb1 itself plus one or two additional linked QTL. tb1 is epistatic to two of these additional QTL for ear traits. Overall, these results provide examples for both a major QTL that maps to a single gene, as well as a case in which a QTL fractionates into multiple linked QTL.     

Quantitative trait loci for hip dysplasia in a crossbreed canine pedigree

Canine hip dysplasia is a common developmental inherited trait characterized by hip laxity, subluxation or incongruity of the femoral head and acetabulum in affected hips. The inheritance pattern is complex and the mutations contributing to trait expression are unknown. In the study reported here, 240 microsatellite markers distributed in 38 autosomes and the X chromosome were genotyped on 152 dogs from three generations of a crossbred pedigree based on trait-free Greyhound and dysplastic Labrador Retriever founders. Interval mapping was undertaken to map the QTL underlying the quantitative dysplastic traits of maximum passive hip laxity (the distraction index), the dorsolateral subluxation score, and the Norberg angle. Permutation testing was used to derive the chromosome-wide level of significance at p < 0.05 for each QTL. Chromosomes 4, 9, 10, 11 (p < 0.01), 16, 20, 22, 25, 29 (p < 0.01), 30, 35, and 37 harbor putative QTL for one or more traits. Successful detection of QTL was due to the crossbreed pedigree, multiple-trait measurements, control of environmental background, and marked advancement in canine mapping tools.     

Shared quantitative trait loci underlying the genetic correlation between continuous traits

We review genetic correlations among quantitative traits in light of their underlying quantitative trait loci (QTL). We derive an expectation of genetic correlation from the effects of underlying loci and test whether published genetic correlations can be explained by the QTL underlying the traits. While genetically correlated traits shared more QTL (33%) on average than uncorrelated traits (11%), the actual number of shared QTL shared was small. QTL usually predicted the sign of the correlation with good accuracy, but the quantitative prediction was poor. Approximately 25% of trait pairs in the data set had at least one QTL with antagonistic effects. Yet a significant minority (20%) of such trait pairs have net positive genetic correlations due to such antagonistic QTL 'hidden' within positive genetic correlations. We review the evidence on whether shared QTL represent single pleiotropic loci or closely linked monotropic genes, and argue that strict pleiotropy can be viewed as one end of a continuum of recombination rates where r=0. QTL studies of genetic correlation will likely be insufficient to predict evolutionary trajectories over long time spans in large panmictic populations, but will provide important insights into the trade-offs involved in population and species divergence.     

Genotype-environment interaction for quantitative trait loci affecting life span in Drosophila melanogaster

The nature of genetic variation for Drosophila longevity in a population of recombinant inbred lines was investigated by estimating quantitative genetic parameters and mapping quantitative trait loci (QTL) for adult life span in five environments: standard culture conditions, high and low temperature, and heat-shock and starvation stress. There was highly significant genetic variation for life span within each sex and environment. In the analysis of variance of life span pooled over sexes and environments, however, the significant genetic variation appeared in the genotype x sex and genotype x environment interaction terms. The genetic correlation of longevity across the sexes and environments was not significantly different from zero in these lines. We estimated map positions and effects of QTL affecting life span by linkage to highly polymorphic roo transposable element markers, using a multiple-trait composite interval mapping procedure. A minimum of 17 QTL were detected; all were sex and/or environment-specific. Ten of the QTL had sexually antagonistic or antagonistic pleiotropic effects in different environments. These data provide support for the pleiotropy theory of senescence and the hypothesis that variation for longevity might be maintained by opposing selection pressures in males and females and variable environments. Further work is necessary to assess the generality of these results, using different strains, to determine heterozygous effects and to map the life span QTL to the level of genetic loci.     

Mapping of shoot fly tolerance loci in sorghum using SSR markers

Sorghum (Sorghum bicolor (L.) Moench) is one of the most important crops in the semiarid regions of the world. One of the important biotic constraints to sorghum production in India is the shoot fly which attacks sorghum at the seedling stage. Identification of the genomic regions containing quantitative trait loci (QTLs) for resistance to shoot fly and the linked markers can facilitate sorghum improvement programmes through marker-assisted selection. A simple sequence repeat (SSR) marker- based skeleton linkage map of two linkage groups of sorghum was constructed in a population of 135 recombinant inbred lines (RIL) derived from a cross between IS18551 (resistant to shoot fly) and 296B (susceptible to shoot fly). A total of 14 SSR markers, seven each on linkage groups A and C were mapped. Using data of different shoot fly resistance component traits, one QTL which is common for glossiness, oviposition and dead hearts was detected following composite interval mapping (CIM) on linkage group A. The phenotypic variation explained by this QTL ranged from 3.8%–6.3%. Besides the QTL detected by CIM, two more QTLs were detected following multi-trait composite interval mapping (MCIM), one each on linkage groups A and C for the combinations of traits which were correlated with each other. Results of the present study are novel as we could find out the QTLs governing more than one trait (pleiotropic QTLs). The identification of pleiotropic QTLs will help in improvement of more than one trait at a time with the help of the same linked markers. For all the QTLs, the resistant parent IS18551 contributed resistant alleles.     

A statistical model for the genetic origin of allometric scaling laws in biology

Many biological processes, from cellular metabolism to population dynamics, are characterized by particular allometric scaling (power-law) relationships between size and rate. Although such allometric relationships may be under genetic determination, their precise genetic mechanisms have not been clearly understood due to a lack of a statistical analytical method. In this paper, we present a basic statistical framework for mapping quantitative genes (or quantitative trait loci, QTL) responsible for universal quarter-power scaling laws of organic structure and function with the entire body size. Our model framework allows the testing of whether a single QTL affects the allometric relationship of two traits or whether more than one linked QTL is segregating. Like traditional multi-trait mapping, this new model can increase the power to detect the underlying QTL and the precision of its localization on the genome. Beyond the traditional method, this model is integrated with pervasive scaling laws to take advantage of the mechanistic relationships of biological structures and processes. Simulation studies indicate that the estimation precision of the QTL position and effect can be improved when the scaling relationship of the two traits is considered. The application of our model in a real example from forest trees leads to successful detection of a QTL governing the allometric relationship of third-year stem height with third-year stem biomass. The model proposed here has implications for genetic, evolutionary, biomedicinal and breeding research.     

Mapping of quantitative trait loci based on growth models

An approach called growth model-based mapping (GMM) of quantitative trait loci (QTLs) is proposed in this paper. The principle of the approach is to fit the growth curve of each individual or line with a theoretical or empirical growth model at first and then map QTLs based on the estimated growth parameters with the method of multiple-trait composite interval mapping. In comparison with previously proposed approaches of QTL mapping based on growth data, GMM has several advantages: (1) it can greatly reduce the amount of phenotypic data for QTL analysis and thus alleviate the burden of computation, particularly when permutation tests or simulation are performed to estimate significance thresholds; (2) it can efficiently analyze unbalanced phenotype data because both balanced and unbalanced data can be used for fitting growth models; and (3) it may potentially help us to better understand the genetic basis of quantitative trait development because the parameters in a theoretical growth model may often have clear biological meanings. A practical example of rice leaf-age development is presented to demonstrate the utility of GMM.     

Verification of QTL for Grain Starch Content and Its Genetic Correlation with Oil Content Using Two Connected RIL Populations in High-Oil Maize

Grain oil content is negatively correlated with starch content in maize in general. In this study, 282 and 263 recombinant inbred lines (RIL) developed from two crosses between one high-oil maize inbred and two normal dent maize inbreds were evaluated for grain starch content and its correlation with oil content under four environments. Single-trait QTL for starch content in single-population and joint-population analysis, and multiple-trait QTL for both starch and oil content were detected, and compared with the result obtained in the two related F_2∶3 populations. Totally, 20 single-population QTL for grain starch content were detected. No QTL was simultaneously detected across all ten cases. QTL at bins 5.03 and 9.03 were all detected in both populations and in 4 and 5 cases, respectively. Only 2 of the 16 joint-population QTL had significant effects in both populations. Three single-population QTL and 8 joint-population QTL at bins 1.03, 1.04–1.05, 3.05, 8.04–8.05, 9.03, and 9.05 could be considered as fine-mapped. Common QTL across F_2∶3 and RIL generations were observed at bins 5.04, 8.04 and 8.05 in population 1 (Pop.1), and at bin 5.03 in population 2 (Pop.2). QTL at bins 3.02–3.03, 3.05, 8.04–8.05 and 9.03 should be focused in high-starch maize breeding. In multiple-trait QTL analysis, 17 starch-oil QTL were detected, 10 in Pop.1 and 7 in Pop.2. And 22 single-trait QTL failed to show significance in multiple-trait analysis, 13 QTL for starch content and 9 QTL for oil content. However, QTL at bins 1.03, 6.03–6.04 and 8.03–8.04 might increase grain starch content and/or grain oil content without reduction in another trait. Further research should be conducted to validate the effect of these QTL in the simultaneous improvement of grain starch and oil content in maize.     

A Multi-Trait,Meta-analysis for Detecting Pleiotropic Polymorphisms for Stature,Fatness and Reproduction in Beef Cattle

Polymorphisms that affect complex traits or quantitative trait loci (QTL) often affect multiple traits. We describe two novel methods (1) for finding single nucleotide polymorphisms (SNPs) significantly associated with one or more traits using a multi-trait, meta-analysis, and (2) for distinguishing between a single pleiotropic QTL and multiple linked QTL. The meta-analysis uses the effect of each SNP on each of n traits, estimated in single trait genome wide association studies (GWAS). These effects are expressed as a vector of signed t-values (t) and the error covariance matrix of these t values is approximated by the correlation matrix of t-values among the traits calculated across the SNP (V). Consequently, t''V⁻¹t is approximately distributed as a chi-squared with n degrees of freedom. An attractive feature of the meta-analysis is that it uses estimated effects of SNPs from single trait GWAS, so it can be applied to published data where individual records are not available. We demonstrate that the multi-trait method can be used to increase the power (numbers of SNPs validated in an independent population) of GWAS in a beef cattle data set including 10,191 animals genotyped for 729,068 SNPs with 32 traits recorded, including growth and reproduction traits. We can distinguish between a single pleiotropic QTL and multiple linked QTL because multiple SNPs tagging the same QTL show the same pattern of effects across traits. We confirm this finding by demonstrating that when one SNP is included in the statistical model the other SNPs have a non-significant effect. In the beef cattle data set, cluster analysis yielded four groups of QTL with similar patterns of effects across traits within a group. A linear index was used to validate SNPs having effects on multiple traits and to identify additional SNPs belonging to these four groups.     

CLIP Test: a new fast,simple and powerful method to distinguish between linked or pleiotropic quantitative trait loci in linkage disequilibria analysis

An important question arises when mapping quantitative trait loci (QTLs) for genetically correlated traits: is the correlation due to pleiotropy (a single QTL affecting more than one trait) and/or close linkage (different QTLs that are physically close to each other and influence the traits)? In this article, we propose the Close Linkage versus Pleiotropism (CLIP) test, a fast, simple and powerful method to distinguish between these two situations. The CLIP test is based on the comparison of the square of the observed correlation between a combination of apparent effects at the marker level to the minimal value it can take under the pleiotropic assumption. A simulation study was performed to estimate the power and alpha risk of the CLIP test and compare it to a test that evaluated whether the confidence intervals of the two QTLs overlapped or not (CI test). On average, the CLIP test showed a higher power (68%) to detect close-linked QTLs than the CI test (43%) and a same alpha risk (4%).