Multiple Quantitative Trait Research Articles

The genetic architecture of seedling resistance to Septoria tritici blotch in the winter wheat doubled-haploid population Solitär × Mazurka

Breeding for resistance to Septoria tritici blotch (STB), caused by Mycosphaerella graminicola (anamorph: Septoria tritici), is an essential component in controlling this important foliar disease of wheat. Inheritance of seedling resistance to seven worldwide pathogen isolates has been studied in a doubled-haploid (DH) population derived from a cross between the field resistant cultivar Solitar and the susceptible cultivar Mazurka. Multiple quantitative trait locus (QTL) mapping revealed major and minor genetic effects on resistance as well as several epistatic relationships in the seedling stage. Solitar conferred resistance to isolate IPO323, governed by Stb6 on chromosome 3A, as well as to IPO99015, IPO92034, Hu1 and Hu2 controlled by a QTL on chromosome arm 1BS, possibly corresponding to Stb11 and minor QTL on chromosomes 1B, 3D, 6B and 7D. Resistance of Mazurka to IPO90015 and BBA22 was caused by a QTL located in a region on 4AL which harbours Stb7 or Stb12. QTL specific to pycnidial coverage on 3B and specific to necrosis on 1A could be discovered for isolate IPO92034. Pairwise epistatic interactions were reliably detected with five isolates. Although their contributions to the total variance are generally low, the genotypic effect of the QTL by QTL interaction of 4AL (Stb7 or Stb12) and 3AS (Stb6) made up almost 15% of disease expression. Altogether, the results suggest a complex inheritance of resistance to STB in the seedling stage in terms of isolate-specificity and resistance mechanisms, which have implications for marker-assisted breeding in an attempt to pyramid STB resistance genes.

Multiple quantitative trait loci influence intra-specific variation in genital morphology between phylogenetically distinct lines of Drosophila montana

The evolution of animal genitalia has gained renewed interest because of their potential roles during sexual selection and early stages of species formation. Although central to understanding the evolutionary process, knowledge of the genetic basis of natural variation in genital morphology is limited to a very few species. Using an outbred cross between phylogenetically distinct lines of Drosophila montana, we characterized quantitative trait loci (QTLs) affecting the size and shape of the distiphallus, a prominent part of the male intromittent organ. Our microsatellite-based linkage analysis shows that intra-specific variation in the distiphallus involves several QTLs of largely additive effect and that a highly significant QTL co-localizes with the same inversion where we have earlier localized a large QTL for a sexually selected courtship song trait. The latter indicates that inversions can play an important role in shaping the evolution of rapidly evolving traits with a potential influence on speciation.

Fast empirical Bayesian LASSO for multiple quantitative trait locus mapping

BackgroundThe Bayesian shrinkage technique has been applied to multiple quantitative trait loci (QTLs) mapping to estimate the genetic effects of QTLs on quantitative traits from a very large set of possible effects including the main and epistatic effects of QTLs. Although the recently developed empirical Bayes (EB) method significantly reduced computation comparing with the fully Bayesian approach, its speed and accuracy are limited by the fact that numerical optimization is required to estimate the variance components in the QTL model.ResultsWe developed a fast empirical Bayesian LASSO (EBLASSO) method for multiple QTL mapping. The fact that the EBLASSO can estimate the variance components in a closed form along with other algorithmic techniques render the EBLASSO method more efficient and accurate. Comparing with the EB method, our simulation study demonstrated that the EBLASSO method could substantially improve the computational speed and detect more QTL effects without increasing the false positive rate. Particularly, the EBLASSO algorithm running on a personal computer could easily handle a linear QTL model with more than 100,000 variables in our simulation study. Real data analysis also demonstrated that the EBLASSO method detected more reasonable effects than the EB method. Comparing with the LASSO, our simulation showed that the current version of the EBLASSO implemented in Matlab had similar speed as the LASSO implemented in Fortran, and that the EBLASSO detected the same number of true effects as the LASSO but a much smaller number of false positive effects.ConclusionsThe EBLASSO method can handle a large number of effects possibly including both the main and epistatic QTL effects, environmental effects and the effects of gene-environment interactions. It will be a very useful tool for multiple QTL mapping.

Caracterização fenotípica e genotípica de caracteres agronômicos em uma população de linhagens recombinantes de aveia (Avena sativa L.)

O melhoramento genético de aveia envolve a seleção de múltiplos caracteres quantitativos e qualitativos. O conhecimento das regiões genômicas que afetam essas características possibilita a seleção assistida por marcadores moleculares. Nesse sentido, o objetivo deste trabalho foi identificar regiões genômicas responsáveis por caracteres quantitativos (QTLs), associadas a marcadores moleculares previamente identificados. O experimento foi conduzido no ano de 2009, sendo empregadas 150 linhagens recombinantes de aveia, oriundas do cruzamento entre os genótipos UFRGS 8 e UFRGS 930605. O mapeamento de QTLs foi realizado através do método por intervalo composto. Foram detectados 16 QTLs para oito dos 9 caracteres avaliados, sendo estes distribuídos em sete grupos de ligação. A porção da variação fenotípica explicada pelos QTLs variou de 7,31% a 16,06%. Importantes QTLs foram identificados para caracteres de interesse aos programas de melhoramento de aveia, como a estatura de planta, o número de dias ao florescimento, o rendimento de grãos, o peso de panícula e o número de grãos por panícula. Os resultados gerados neste trabalho fornecem subsídios aos programas de melhoramento genético de aveia, proporcionando um maior entendimento dos mecanismos genéticos envolvidos com os caracteres de interesse. No entanto, estes resultados devem ser validados em outras populações para que a seleção assistida por marcadores moleculares possa ser realizada com sucesso.

Genomic value prediction for quantitative traits under the epistatic model

BackgroundMost quantitative traits are controlled by multiple quantitative trait loci (QTL). The contribution of each locus may be negligible but the collective contribution of all loci is usually significant. Genome selection that uses markers of the entire genome to predict the genomic values of individual plants or animals can be more efficient than selection on phenotypic values and pedigree information alone for genetic improvement. When a quantitative trait is contributed by epistatic effects, using all markers (main effects) and marker pairs (epistatic effects) to predict the genomic values of plants can achieve the maximum efficiency for genetic improvement.ResultsIn this study, we created 126 recombinant inbred lines of soybean and genotyped 80 makers across the genome. We applied the genome selection technique to predict the genomic value of somatic embryo number (a quantitative trait) for each line. Cross validation analysis showed that the squared correlation coefficient between the observed and predicted embryo numbers was 0.33 when only main (additive) effects were used for prediction. When the interaction (epistatic) effects were also included in the model, the squared correlation coefficient reached 0.78.ConclusionsThis study provided an excellent example for the application of genome selection to plant breeding.

Identification of interacting modifier loci of liver fibrosis in Abcb4/Mdr2 deficient mice

Functional variants in the ATP-binding cassette transporter gene ABCB4, have been identified in various cholestatic liver diseases, including progressive intrahepatic familial cholestasis. Phenotypic presentations of patients harbouring ABCB4 mutations differ markedly, and to date no modifier loci have been identified in human cohorts. We recently reported a single modifier locus on chromosome (chr) 4 (Grünhage et al. AASLD 2008) in a backcross of Abcb4 deficient mice. We now analysed these mice with regards to locus interactions influencing liver fibrosis.Fibrosis was monitored in an F2 cross of fibrosis-susceptible BALB-Abcb4-/- and resistant FVB-Abcb4-/- mice. As a quantitative trait, we determined hepatic collagen contents at 16 weeks of age. The F2 mice were genotyped for 270 single nucleotide polymorphisms, and mapped for multiple quantitative trait loci (QTL) that determine liver fibrosis, using R/qtl. We generated 183 F2 mice that displayed marked variation of biliary fibrosis and hepatic collagen accumulation (mean 300.9µg/g, range 72.9–778.5µg/g). Consistent with our previous results, we detected a potential single locus on chr 4 (LOD 2.1). Pairwise QTL scans identified two interactions between loci on chr 2 and 9 as well as chr 11 and 12. Composite interval mapping showed that the maker on chr 12 (rs13481500) was significantly associated with liver fibrosis dependent on the interacting locus at chr 11 (rs6346368; LOD 5.06). Permutation tests confirmed this marker as a significant modifier locus (pgw<0.05). Homozygous carriers of the risk alleles showed significantly increased collagen contents (489.4 vs.300.2µg/g, p<0.05). In addition to our previously reported single QTL on chr 4, we identified two interacting loci on chr 11 and 12, influencing fibrosis progression in Abcb4-/- mice. Fine mapping experiments to identify candidate genes at these loci may contribute to a better understanding of phenotypic heterogeneity in humans with ABCB4 deficiency.

Blood pressure and proteinuria effects of multiple quantitative trait loci on rat chromosome 9 that differentiate the spontaneously hypertensive rat from the Dahl salt-sensitive rat: Erratum

Blood pressure and proteinuria effects of multiple quantitative trait loci on rat chromosome 9 that differentiate the spontaneously hypertensive rat from the Dahl salt-sensitive rat: Erratum

Detecting Pleiotropy and Epistasis Using Variance Components Linkage Analysis in jPAP

jPAP (Java Pedigree Analysis Package) performs variance components linkage analysis of either quantitative or discrete traits. Multivariate linkage analysis of two or more traits (all quantitative, all discrete, or any combination) allows the inference of pleiotropy between the traits. The inclusion of multiple quantitative trait loci in linkage analysis allows the inference of epistasis between loci. A user-friendly graphical user interface facilitates the usage of jPAP.

Extended Bayesian LASSO for multiple quantitative trait loci mapping and unobserved phenotype prediction.

The Bayesian LASSO (BL) has been pointed out to be an effective approach to sparse model representation and successfully applied to quantitative trait loci (QTL) mapping and genomic breeding value (GBV) estimation using genome-wide dense sets of markers. However, the BL relies on a single parameter known as the regularization parameter to simultaneously control the overall model sparsity and the shrinkage of individual covariate effects. This may be idealistic when dealing with a large number of predictors whose effect sizes may differ by orders of magnitude. Here we propose the extended Bayesian LASSO (EBL) for QTL mapping and unobserved phenotype prediction, which introduces an additional level to the hierarchical specification of the BL to explicitly separate out these two model features. Compared to the adaptiveness of the BL, the EBL is "doubly adaptive" and thus, more robust to tuning. In simulations, the EBL outperformed the BL in regard to the accuracy of both effect size estimates and phenotypic value predictions, with comparable computational time. Moreover, the EBL proved to be less sensitive to tuning than the related Bayesian adaptive LASSO (BAL), which introduces locus-specific regularization parameters as well, but involves no mechanism for distinguishing between model sparsity and parameter shrinkage. Consequently, the EBL seems to point to a new direction for QTL mapping, phenotype prediction, and GBV estimation.

The genetic architecture of domestication in the chicken: effects of pleiotropy and linkage

The extent of pleiotropy and epistasis in quantitative traits remains equivocal. In the case of pleiotropy, multiple quantitative trait loci are often taken to be pleiotropic if their confidence intervals overlap, without formal statistical tests being used to ascertain if these overlapping loci are statistically significantly pleiotropic. Additionally, the degree to which the genetic correlations between phenotypic traits are reflected in these pleiotropic quantitative trait loci is often variable, especially in the case of antagonistic pleiotropy. Similarly, the extent of epistasis in various morphological, behavioural and life-history traits is also debated, with a general problem being the sample sizes required to detect such effects. Domestication involves a large number of trade-offs, which are reflected in numerous behavioural, morphological and life-history traits which have evolved as a consequence of adaptation to selective pressures exerted by humans and captivity. The comparison between wild and domestic animals allows the genetic analysis of the traits that differ between these population types, as well as being a general model of evolution. Using a large F(2) intercross between wild and domesticated chickens, in combination with a dense SNP and microsatellite marker map, both pleiotropy and epistasis were analysed. The majority of traits were found to segregate in 11 tight 'blocks' and reflected the trade-offs associated with domestication. These blocks were shown to have a pleiotropic 'core' surrounded by more loosely linked loci. In contrast, epistatic interactions were almost entirely absent, with only six pairs identified over all traits analysed. These results give insights both into the extent of such blocks in evolution and the development of domestication itself.

Multiple linked quantitative trait loci within the Tmevd2/Eae3 interval control the severity of experimental allergic encephalomyelitis in DBA/2J mice

Theiler’s murine encephalomyelitis virus-induced demyelination (TMEVD) and experimental allergic encephalomyelitis (EAE) are the principal animal models of multiple sclerosis (MS). Previously we provided evidence that Tmevd2 and Eae3 may represent either a shared susceptibility locus or members of a gene complex controlling susceptibility to CNS inflammatory demyelinating disease. To explore the genetic relationship between Tmevd2 and Eae3, we generated a D2.C-Tmevd2 interval-specific congenic (ISC) line and three overlapping interval-specific recombinant congenic (ISRC) lines in which the Tmevd2 resistant allele from BALB/cByJ was introgressed onto the TMEVD-susceptible DBA/2J background. These mice, all H2d, were studied for susceptibility to EAE elicited by immunization with PLP180–199. Compared to DBA/2J mice, D2.C-Tmevd2 mice developed a significantly less severe clinical disease course and EAE pathology in the spinal cord, confirming the existence of Eae3 and its linkage to Tmevd2 in this strain combination. Compare to DBA/2J and D2.C-Tmevd2, all three ISRC lines exhibited clinical disease courses of intermediate severity. Neither differences in ex vivo antigen-specific cytokine nor proliferative responses uniquely cosegregated with differences in disease severity. These results indicate that multiple QTL within the Tmevd2/Eae3 interval influence EAE severity, one of which includes a homology region for a QTL found in MS by admixture mapping.

Nonparametric Bayesian Variable Selection With Applications to Multiple Quantitative Trait Loci Mapping With Epistasis and Gene–Environment Interaction

The joint action of multiple genes is an important source of variation for complex traits and human diseases. However, mapping genes with epistatic effects and gene-environment interactions is a difficult problem because of relatively small sample sizes and very large parameter spaces for quantitative trait locus models that include such interactions. Here we present a nonparametric Bayesian method to map multiple quantitative trait loci (QTL) by considering epistatic and gene-environment interactions. The proposed method is not restricted to pairwise interactions among genes, as is typically done in parametric QTL analysis. Rather than modeling each main and interaction term explicitly, our nonparametric Bayesian method measures the importance of each QTL, irrespective of whether it is mostly due to a main effect or due to some interaction effect(s), via an unspecified function of the genotypes at all candidate QTL. A Gaussian process prior is assigned to this unknown function. In addition to the candidate QTL, nongenetic factors and covariates, such as age, gender, and environmental conditions, can also be included in the unspecified function. The importance of each genetic factor (QTL) and each nongenetic factor/covariate included in the function is estimated by a single hyperparameter, which enters the covariance function and captures any main or interaction effect associated with a given factor/covariate. An initial evaluation of the performance of the proposed method is obtained via analysis of simulated and real data.

An EST-SSR Marker Tightly Linked to the Barley Male Sterility Gene (msg6) Located on Chromosome 6H

The barley male sterility gene (msg6) located on chromosome 6H has been used in breeding and research since its discovery 7 decades ago, but to date, no research has been reported that linked the gene with molecular markers. The main objective of this study was to identify expressed sequence tag-simple sequence repeat (EST-SSR) markers linked to msg6 as this could provide opportunities for gene discovery. In a cross of a male sterile line (04-042B) with a fully fertile line (VB0330; VB9524/Mundah), male sterility segregated in a 3:1 ratio of fertile to completely sterile plants (χ(2) = 0.03, P(0.05) = 0.95), in a population of 250 F(2) plants. Multipoint linkage mapping placed the msg6 gene at 4.9 cM from the EST-SSR, GBM1267, whereas 2-point analysis estimated a recombination fraction of 0.05 ± 0.02 (logarithm of the odds score = 26.34) between the EST-SSR and the male sterility gene. Multiple interval quantitative trait locus (QTL) analysis of spike weight, an indicative measure of reproductive success, identified a QTL near GBM1267 as having a major influence, explaining 68.7% of the variation in weight of individual spikes. The GBM1267 marker segregated in a 1:2:1 ratio, which makes it highly desirable for marker-assisted selection, as it can distinguish the recessive from the dominant and from heterozygous individuals. Another EST-SSR marker, designated VBMS103, was developed in the present study to provide an additional marker with known sequence (AL501881) close to the msg6 gene. The results provide highly informative functional tools for tracking the msg6 gene in breeding programs.

A genome-wide linkage scan identifies multiple quantitative trait loci for HDL-cholesterol levels in families with premature CAD and MI

Plasma HDL cholesterol levels (HDL-C) are an independent predictor of coronary artery disease (CAD). We have completed a genome-wide linkage scan for HDL-C in a US cohort consisting of 388 multiplex families with premature CAD (GeneQuest). The heritability of HDL-C in GeneQuest was 0.37 with gender and age as covariates (P = 5.1 x 10(-4)). Two major quantitative trait loci (QTL) for log-transformed HDL-C adjusted for age and gender were identified onto chromosomes 7p22 and 15q25 with maximum multipoint logarithm of odds (LOD) scores of 3.76 and 6.69, respectively. Fine mapping decreased the 7p22 LOD score to a nonsignificant level of 3.09 and split the 15q25 QTL into two loci, one minor QTL on 15q22 (LOD = 2.73) that spanned the LIPC gene, and the other at 15q25 (LOD = 5.63). A family-based quantitative transmission disequilibrium test (QTDT) revealed significant association between variant rs1800588 in LIPC and HDL-C in the GeneQuest population (P = 0.0067), which may account for the minor QTL on 15q22. The 15q25 QTL is the most significant locus identified for HDL-C to date, and these results provide a framework for the ultimate identification of the underlying HDL-C variant and gene on chromosomes 15q25, which will provide insights into novel regulatory mechanisms of HDL-C metabolism.

Bayesian shrinkage mapping of quantitative trait loci in variance component models

BackgroundIn this article, I propose a model-selection-free method to map multiple quantitative trait loci (QTL) in variance component model, which is useful in outbred populations. The new method can estimate the variance of zero-effect QTL infinitely to zero, but nearly unbiased for non-zero-effect QTL. It is analogous to Xu's Bayesian shrinkage estimation method, but his method is based on allelic substitution model, while the new method is based on the variance component models.ResultsExtensive simulation experiments were conducted to investigate the performance of the proposed method. The results showed that the proposed method was efficient in mapping multiple QTL simultaneously, and moreover it was more competitive than the reversible jump MCMC (RJMCMC) method and may even out-perform it.ConclusionsThe newly developed Bayesian shrinkage method is very efficient and powerful for mapping multiple QTL in outbred populations.

Recent Advances in Genetics of the Spontaneously Hypertensive Rat

The spontaneously hypertensive rat (SHR) is the most widely used animal model of essential hypertension and associated metabolic disturbances. Multiple quantitative trait loci associated with hemodynamic and metabolic parameters have been mapped in the SHR. Recently, it has become possible to identify some of the specific quantitative trait gene (QTG) variants that underlie quantitative trait loci linked to complex cardiovascular and metabolic traits in SHR related strains. Recombinant inbred strains derived from SHR and Brown Norway progenitors, together with SHR congenic and transgenic strains, have proven useful for establishing the identity of several QTGs in SHR models. It is anticipated that the combined use of linkage analyses and gene expression profiles, together with the recently available genome sequences of both the SHR and Brown Norway strains and new methods for manipulating the rat genome, will soon accelerate progress in identifying QTGs for complex traits in SHR-related strains.

Locating Multiple Interacting Quantitative Trait Loci with the Zero-Inflated Generalized Poisson Regression

We consider the problem of locating multiple interacting quantitative trait loci (QTL) influencing traits measured in counts. In many applications the distribution of the count variable has a spike at zero. Zero-inflated generalized Poisson regression (ZIGPR) allows for an additional probability mass at zero and hence an improvement in the detection of significant loci. Classical model selection criteria often overestimate the QTL number. Therefore, modified versions of the Bayesian Information Criterion (mBIC and EBIC) were successfully used for QTL mapping. We apply these criteria based on ZIGPR as well as simpler models. An extensive simulation study shows their good power detecting QTL while controlling the false discovery rate. We illustrate how the inability of the Poisson distribution to account for over-dispersion leads to an overestimation of the QTL number and hence strongly discourages its application for identifying factors influencing count data. The proposed method is used to analyze the mice gallstone data of Lyons et al. (2003). Our results suggest the existence of a novel QTL on chromosome 4 interacting with another QTL previously identified on chromosome 5. We provide the corresponding code in R.

Nonparametric Bayesian Variable Selection With Applications to Multiple Quantitative Trait Loci Mapping With Epistasis and Gene–Environment Interaction

Nonparametric Bayesian Variable Selection With Applications to Multiple Quantitative Trait Loci Mapping With Epistasis and Gene–Environment Interaction

Bivariate association analysis for quantitative traits using generalized estimation equation

Quantitative traits often underlie risk for complex diseases. Many studies collect multiple correlated quantitative phenotypes and perform univariate analyses on each of them respectively. However, this strategy may not be powerful and has limitations to detect pleiotropic genes that may underlie correlated quantitative traits. In addition, testing multiple traits individually will exacerbate perplexing problem of multiple testing. In this study, generalized estimating equation 2 (GEE2) is applied to association mapping of two correlated quantitative traits. We suppose that a quantitative trait locus is located in a chromosome region that exerts pleiotropic effects on multiple quantitative traits. In that region, multiple SNPs are genotyped. Genotypes of these SNPs and the two quantitative traits affected by a causal SNP were simulated under various parameter values: residual correlation coefficient between two traits, causal SNP heritability, minor allele frequency of the causal SNP, extent of linkage disequilibrium with the causal SNP, and the test sample size. By power analytical analyses, it is showed that the bivariate method is generally more powerful than the univariate method. This method is robust and yields false-positive rates close to the pre-set nominal significance level. Our real data analyses attested to the usefulness of the method.

Molecular detection of genomic regions associated with grain yield and yield-related components in an elite bread wheat cross evaluated under irrigated and rainfed conditions

Grain yield and grain weight of wheat are often decreased by water-limitation in the north-eastern cropping belt of Australia. Based on knowledge that CIMMYT lines are well-adapted in this region, a recombinant inbred line (RIL) population between two elite CIMMYT bread wheats (Seri M82 and Babax) was evaluated under water-limited environments. Fourteen productivity traits were evaluated in 192 progeny in up to eight trials. For three aggregations of the environments (all, high yield or low yield), multiple quantitative trait loci (QTL) were detected, each explaining <15% of variation. Co-location of multiple trait QTL was greatest on linkage groups 1B-a, 1D-b, 4A-a, 4D-a, 6A-a, 6B-a, 7A-a and an unassigned linkage group. Two putative QTL (LOD > 3) from Seri (6D-b and UA-d) increased grain yield and co-located with a suggestive (2 < LOD < 3) and a putative QTL for increased stem carbohydrate content (WSC), respectively; the latter QTL also co-located with a putative anthesis QTL for earlier flowering. Both QTL were detected only in high yield (>4t ha(-1)) environments. A third increased grain yield QTL (7A-a) from Babax co-located with QTL for increased grain number. Six putative QTL increased grain weight and co-located with QTL for harvest index, grains per spike and spike number. Three putative QTL for increased grains per spike co-located with strong QTL for earlier flowering, increased grain weight and fewer spikes. A group of progeny that exceeded the mean grain yield and grain weight of commercial checks had an increased frequency of QTL for high WSC, large grain size, increased harvest index and greater height, but fewer stems, when compared to low yielding (20% less), low grain weight progeny. These findings were consistent with agronomic analyses of the germplasm and demonstrate that there should be opportunities to independently manipulate grain number and grain size which is typically difficult due to strong negative correlations.