A nonparametric bootstrap method for testing close linkage vs. pleiotropy of coincident quantitative trait loci

Development and Genetic Characterization of Advanced Backcross Materials and An Introgression Line Population of Solanum incanum in a S. melongena Background

Advanced backcrosses (ABs) and introgression lines (ILs) of eggplant (Solanum melongena) can speed up genetics and genomics studies and breeding in this crop. We have developed the first full set of ABs and ILs in eggplant using Solanum incanum, a wild eggplant that has a relatively high tolerance to drought, as a donor parent. The development of these ABs and IL eggplant populations had a low efficiency in the early stages, because of the lack of molecular markers and genomic tools. However, this dramatically improved after performing genotyping-by-sequencing in the first round of selfing, followed by high-resolution-melting single nucleotide polymorphism genotyping in subsequent selection steps. A set of 73 selected ABs covered 99% of the S. incanum genome, while 25 fixed immortal ILs, each carrying a single introgressed fragment in homozygosis, altogether spanned 61.7% of the S. incanum genome. The introgressed size fragment in the ILs contained between 0.1 and 10.9% of the S. incanum genome, with a mean value of 4.3%. Sixty-eight candidate genes involved in drought tolerance were identified in the set of ILs. This first set of ABs and ILs of eggplant will be extremely useful for the genetic dissection of traits of interest for eggplant, and represents an elite material for introduction into the breeding pipelines for developing new eggplant cultivars adapted to the challenges posed by the climate-change scenario.

The roles of pleiotropy and close linkage as revealed by association mapping of yield and correlated traits of wheat (Triticum aestivum L.)

Grain yield (GY) of bread wheat (Triticum aestivum L.) is quantitatively inherited. Correlated GY-syndrome traits such as plant height (PH), heading date (HD), thousand grain weight (TGW), test weight (TW), grains per ear (GPE), and ear weight (EW) influence GY. Most quantitative genetics studies assessed the multiple-trait (MT) complex of GY-syndrome using single-trait approaches, and little is known about its underlying pleiotropic architecture. We investigated the pleiotropic architecture of wheat GY-syndrome through MT association mapping (MT-GWAS) using 372 varieties phenotyped in up to eight environments and genotyped with 18 832 single nucleotide polymorphisms plus 24 polymorphic functional markers. MT-GWAS revealed a total of 345 significant markers spread genome wide, representing 8, 40, 11, 40, 34, and 35 effective GY-PH, GY-HD, GY-TGW, GY-TW, GY-GPE, and GY-EW associations, respectively. Among them, pleiotropic roles of Rht-B1 and TaGW2-6B loci were corroborated. Only one marker presented simultaneous associations for three traits (i.e. GY-TGW-TW). Close linkage was difficult to differentiate from pleiotropy; thus, the pleiotropic architecture of GY-syndrome was dissected more as a cause of pleiotropy rather than close linkage. Simulations showed that minor allele frequencies, along with sizes and distances between quantitative trait loci for two traits, influenced the ability to distinguish close linkage from pleiotropy.

An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance

Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes.

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%).

The many faces of pleiotropy

Pleiotropy is the well-established phenomenon of a single gene affecting multiple traits. It has long played a central role in theoretical, experimental, and clinical research in genetics, development, molecular biology, evolution, and medicine. In recent years, genomic techniques have brought data to bear on fundamental questions about the nature and extent of pleiotropy. However, these efforts are plagued by conceptual difficulties derived from disparate meanings and interpretations of pleiotropy. Here, we describe distinct uses of the pleiotropy concept and explain the pitfalls associated with applying empirical data to them. We conclude that, for any question about the nature or extent of pleiotropy, the appropriate answer is always 'What do you mean?'.

Genotype X diet interactions in mice predisposed to mammary cancer: II. Tumors and metastasis

High dietary fat intake and obesity may increase the risk of susceptibility to certain forms of cancer. To study the interactions of dietary fat, obesity, and metastatic mammary cancer, we created a population of F(2) mice cosegregating obesity QTL and the MMTV-PyMT transgene. We fed the F(2) mice either a very high-fat or a matched-control-fat diet, and we measured growth, body composition, age at mammary tumor onset, tumor number and severity, and formation of pulmonary metastases. SNP genotyping across the genome facilitated analyses of QTL and QTL x diet interaction effects. Here we describe effects of diet on mammary tumor and metastases phenotypes, mapping of tumor/metastasis modifier genes, and the interaction between dietary fat levels and effects of cancer modifiers. Results demonstrate that animals fed a high-fat diet are not only more likely to experience decreased mammary cancer latency but increased tumor growth and pulmonary metastases occurrence over an equivalent time. We identified 25 modifier loci for mammary cancer and pulmonary metastasis, likely representing 13 unique loci after accounting for pleiotropy, and novel QTL x diet interactions at a majority of these loci. These findings highlight the importance of accurately modeling not only the human cancer characteristics in mice but also the environmental exposures of human populations.

Genotype X diet interactions in mice predisposed to mammary cancer. I. Body weight and fat

High dietary fat intake and obesity may increase susceptibility to certain forms of cancer. To study the interactions of dietary fat, obesity, and metastatic mammary cancer, we created a population of F(2) mice cosegregating obesity QTL and the MMTV-PyMT transgene. We fed the F(2) mice either a very-high-fat or a matched-control-fat diet and measured growth, body composition, age at mammary tumor onset, tumor number and severity, and formation of pulmonary metastases. SNP genotyping across the genome facilitated analyses of QTL and QTL x diet interaction effects. Here we describe development of the F(2) population (n = 615) which resulted from a cross between the polygenic obesity model M16i and FVB/NJ-TgN (MMTV-PyMT)(634Mul), effects of diet on growth and body composition, and QTL and QTL x diet and/or gender interaction effects for growth and obesity-related phenotypes. We identified 38 QTL for body composition traits that were significant at the genome-wide 0.05 level, likely representing nine distinct loci after accounting for pleiotropic effects. QTL x diet and/or gender interactions were present at 15 of these QTL, indicating that such interactions play a significant role in defining the genetic architecture of complex traits such as body weight and obesity.

Analysis of quantitative trait loci

Diseases with complex inheritance are characterized by multiple genetic and environmental factors that often interact to produce clinical symptoms. In addition, etiological heterogeneity (different risk factors causing similar phenotypes) obscure the inheritance pattern among affected relatives and hamper the feasibility of gene-mapping studies. For such diseases, the careful selection of quantitative phenotypes that may represent intermediary risk factors for disease development (intermediate phenotypes) is etiologically more homogeneous than the disease per se. Over the last 15 years quantitative trait locus mapping has become a popular method for understanding the genetic basis for intermediate phenotypes. This chapter provides an introduction to classical and recent strategies for mapping quantitative trait loci in humans.

Environmental influence on the genetic correlations between life-history traits in Caenorhabditis elegans

Empirical evidence is mounting to suggesting that genetic correlations between life-history traits are environment specific. However, detailed knowledge about the loci underlying genetic correlations in different environments is scant. Here, we studied the influence of temperature (12 degrees C and 24 degrees C) on the genetic correlations between egg size, egg number and body mass in the nematode Caenorhabditis elegans. We used a quantitative trait loci (QTL) approach based on a genetic map with evenly spaced single nucleotide polymorphism markers in an N2 x CB4856 recombinant inbred panel. Significant genetic correlations between various traits were found at both temperatures. We detected pleiotropic or closely linked QTL, which supported the negative correlation between egg size and egg number at 12 degrees C, the positive correlation across temperatures for body mass, and the positive correlation between body mass and egg size at 12 degrees C. The results indicate that specific loci control the covariation in these life-history traits and the locus control is prone to environmental conditions.

Development of a genomic library of near isogenic lines (NILs) in melon (Cucumis melo L.) from the exotic accession PI161375

A doubled haploid line (DHL) population of melon derived from a cross between the Korean cultivar "Songwhan Charmi" accession PI161375 (SC), included in the horticultural group conomon, and the Spanish cultivar "Piel de Sapo" (PS), included in the horticultural group inodorus, was used to develop a collection of near isogenic lines (NILs). These parental lines represent very different melon cultivar groups, with important differences at fruit, plant, disease response and molecular level. This cross is one of the most polymorphic ones within melon germplasm. Selected DHLs were backcrossed to PS and further backcrossing and selfing was performed, monitoring introgressions from SC using molecular markers covering the melon genetic map. A final collection of 57 NILs was obtained, containing a unique independent introgression from SC in the PS genetic background. The introgressions within the collection cover at least 85% of the SC genome with an average introgression size of 41 cM, corresponding to 3.4% of the SC genome. The average resolution for mapping genes or quantitative trait loci is 18.90 cM. This set of NILs is a potentially powerful tool for the study of quantitative trait locus involved in melon fruit quality and other important complex traits, and the introduction of new genetic variability in modern cultivars from exotic sources. The NILs can also be used as pre-competitive breeding lines in melon breeding projects.

Testing hypotheses regarding the genetics of adaptation

Many of the hypotheses regarding the genetics of adaptation require that one know specific details about the genetic basis of complex traits, such as the number and effects of the loci involved. Developments in molecular biology have made it possible to create relatively dense maps of markers that can potentially be used to map genes underlying specific traits. However, there are a number of reasons to doubt that such mapping will provide the level of resolution necessary to specifically address many evolutionary questions. Moreover, evolutionary change is built upon the substitution of individual mutations, many of which may now be cosegregating in the same allele. In order for this developing area not to become a mirage that traps the efforts of an entire field, the genetic dissection of adaptive traits should be conducted within a strict hypothesis-testing framework and within systems that promise a reasonable chance of identifying the specific genetic changes of interest. Continuing advances in molecular technology may lead the way here, but some form of genetic testing is likely to be forever required.

Whole genome scan to detect chromosomal regions affecting multiple traits in dairy cattle

Chromosomal regions affecting multiple traits (multiple trait quantitative trait regions or MQR) in dairy cattle were detected using a method based on results from single trait analyses to detect quantitative trait loci (QTL). The covariance between contrasts for different traits in single trait regression analysis was computed. A chromosomal region was considered an MQR when the observed covariance between contrasts deviated from the expected covariance under the null hypothesis of no pleiotropy or close linkage. The expected covariance and the confidence interval for the expected covariance were determined by permutation of the data. Four categories of traits were analyzed: production (5 traits), udder conformation (6 traits), udder health (2 traits), and fertility (2 traits). The analysis of a granddaughter design involving 833 sons of 20 grandsires resulted in 59 MQR (alpha = 0.01, chromosomewise). Fifteen MQR were found on Bos taurus autosome (BTA) 14. Four or more MQR were found on BTA 6, 13, 19, 22, 23, and 25. Eight MQR involving udder conformation and udder health and 4 MQR involving production traits and udder health were found. Five MQR were identified for combinations of fertility and udder conformation traits, and another 5 MQR were identified for combinations of fertility and production traits. For 22 MQR, the difference between the correlation attributable to the MQR and the overall genetic correlation was >0.60. Although the false discovery rate was relatively high (0.52), it was considered important to present these results to assess potential consequences of using these MQR for marker-assisted selection.

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.

Search for pleiotropic QTL on chromosome BTA6 affecting yield traits of milk production

The primary aim of this study was to investigate whether previous findings of similar quantitative trait loci (QTL) positions for correlated yield traits are due to a pleiotropic QTL. We applied a multitrait variance component based QTL mapping method to a dataset involving five granddaughter families from the German Holstein dairy cattle population. The marker map contained 16 microsatellite markers, distributed across chromosome BTA6. A chromosomewise significance threshold was used, because BTA6 is known to harbor QTL for several milk traits. To evaluate the results from the multivariate, across-family analysis, we also conducted single-family analyses using the least squares method of QTL estimation. The results provided two significant QTL findings at 49 and 64 cM for milk yield in different families and putative QTL at 68 cM for fat yield and at 71 cM for protein yield in another family. The results for fat and protein yield were confirmed by a univariate, across-family variance components analysis. The multivariate analysis of three bivariate trait combinations resulted in a significant pleiotropic QTL finding at 68 cM for fat yield and protein yield, bracketed by markers TGLA37 and FBN13. The estimates of variance contribution due to this QTL were 23% and 25%, respectively.

Testing life-history pleiotropy in Caenorhabditis elegans

Much life-history theory assumes that alleles segregating in natural populations pleiotropically affect life-history traits. This assumption, while plausible, has rarely been tested directly. Here we investigate the genetic relationship between two traits often suggested to be connected by pleiotropy: maternal body size and fertility. We carry out a quantitative trait locus (QTL) analysis on two isolates of the free-living nematode Caenorhabditis elegans, and identify two body size and three fertility QTLs. We find that one of the fertility QTLs colocalizes with the two body size QTLs on Chromosome IV. Further analysis, however, shows that these QTLs are genetically separable. Thus, none of the five body size or fertility QTLs identified here shows detectable pleiotropy for the assayed traits. The evolutionary origin of these QTLs, possible candidate loci, and the significance for life-history evolution are discussed.

QTL analysis identifies multiple behavioral dimensions in ethological tests of anxiety in laboratory mice

BACKGROUND

Ethological tests of anxiety-related behaviors, such as the open field arena and elevated plus maze, are often carried out on transgenic animals in the attempt to correlate gene function with a behavioral phenotype. However, the interpretation of such tests is problematic, as it is probable that different tests measure different aspects of behavior; indeed, anxiety may not be a unitary phenomenon. Here, we address these questions by asking whether behaviors in five ethological tests of anxiety are under the influence of a common set of genes.

RESULTS

Using over 1600 F2 intercross animals, we demonstrate that separate, but overlapping, genetic effects can be detected that influence different behavioral dimensions in the open field, elevated plus maze, square maze, light-dark box, and mirror chamber. We find quantitative trait loci (QTLs) on chromosomes 1, 4, and 15 that operate in four tests of anxiety but can be differentiated by their action on behavior in threatening and nonthreatening environments and by whether habituation of the animals to an aversive environment alters their influence. QTLs on chromosomes 7, 12, 14, 18, and X influenced a subset of behavioral measures.

CONCLUSIONS

The chromosome 15 QTL acts primarily on avoidance behavior, the chromosome 1 QTL influences exploration, and the QTL on chromosome 4 influences activity. However, the effects of loci on other chromosomes are not so readily reconciled with our current understanding of the psychology of anxiety. Genetic effects on behaviors in these tests are more complex than expected and may not reflect an influence on anxiety.

Enhanced efficiency of quantitative trait loci mapping analysis based on multivariate complexes of quantitative traits

An approach to increase the efficiency of mapping quantitative trait loci (QTL) was proposed earlier by the authors on the basis of bivariate analysis of correlated traits. The power of QTL detection using the log-likelihood ratio (LOD scores) grows proportionally to the broad sense heritability. We found that this relationship holds also for correlated traits, so that an increased bivariate heritability implicates a higher LOD score, higher detection power, and better mapping resolution. However, the increased number of parameters to be estimated complicates the application of this approach when a large number of traits are considered simultaneously. Here we present a multivariate generalization of our previous two-trait QTL analysis. The proposed multivariate analogue of QTL contribution to the broad-sense heritability based on interval-specific calculation of eigenvalues and eigenvectors of the residual covariance matrix allows prediction of the expected QTL detection power and mapping resolution for any subset of the initial multivariate trait complex. Permutation technique allows chromosome-wise testing of significance for the whole trait complex and the significance of the contribution of individual traits owing to: (a) their correlation with other traits, (b) dependence on the chromosome in question, and (c) both a and b. An example of application of the proposed method on a real data set of 11 traits from an experiment performed on an F(2)/F(3) mapping population of tetraploid wheat (Triticum durum x T. dicoccoides) is provided.

Multitrait least squares for quantitative trait loci detection

A multiple-trait QTL mapping method using least squares is described. It is presented as an extension of a single-trait method for use with three-generation, outbred pedigrees. The multiple-trait framework allows formal testing of whether the same QTL affects more than one trait (i.e., a pleiotropic QTL) or whether more than one linked QTL are segregating. Several approaches to the testing procedure are presented and their suitability discussed. The performance of the method is investigated by simulation. As previously found, multitrait analyses increase the power to detect a pleiotropic QTL and the precision of its location estimate. With enough information, discrimination between alternative genetic models is possible.

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.

QTL analysis in plants; where are we now?

We have briefly reviewed the methods currently available for QTL analysis in segregating populations and summarized some of the conclusions arising from such analyses in plant populations. We show that the analytical methods locate QTL with poor precision (10-30 cM), unless the heritability of an individual QTL is high. Also the estimates of the QTL effects, particularly the dominance effects tend to be inflated because only large estimates are significant. Estimates of numbers of QTL per trait are generally low (< 8) for individual trials. This may suggest that there are few QTL but probably reflects the power of the methods. There is no large correlation between the numbers of QTL found and the amount of the variation explained. Of those cases where dominance is measurable, dominance ratios are often > 1, but seldom significantly greater. These latter cases need further analysis. Many QTL map close to candidate genes, and there is growing evidence from synteny studies of corresponding chromosome regions carrying similar QTL in different species. However, unreliability of QTL location may suggest false candidates.