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

Genotypic variation and covariation in wheat seedling seminal root architecture and grain yield under field conditions

Greater embryo size in a large and carefully phenotyped mapping population was genetically associated with a greater number of longer seminal roots to increase grain yield in droughted field environments. Breeding modification of root architecture is challenging in field environments owing to genetic and phenotypic complexity, and poor repeatability with root sampling. Seeds from a large mapping population varying in embryo size were harvested from a common glasshouse and standardised to a common size before assessing in rolled germination paper at 12 and 20 °C for seedling growth. Differences in genotype means were large and heritabilities high (h² = 0.55-0.93) indicating strong and repeatable genotypic differences for most root traits. Seminal roots 1 to 3 were produced on all seedlings, whereas growth of seminal roots 4, 5 and 6 was associated with differences in embryo size. Increases in seminal root number from 4 to 6 per plant were strongly, genetically correlated with increases in total seminal length (r_g = 0.84, < 0.01). Multivariate analysis confirmed initiation and growth of seminal roots 1, 2 and 3, and of roots 4, 5 and 6 behaved as genetically independent (r_Pg = 0.15 ns) cohorts. Tails representing extremes in seedling root length and number were associated with significant differences in grain yield of up to 35% in droughted field environments but were not different in irrigated environments. Increases in grain yield were linked to greater lengths of seminal roots 4, 5 and 6 and were largely independent of plant height or development. This is the first report on the genetic relationship of seedling root architecture and embryo size, and potential in selection of seminal root size for accessing deep-soil moisture in droughted environments.

Identification, High-Density Mapping, and Characterization of New Major Powdery Mildew Resistance Loci From the Emmer Wheat Landrace GZ1

Powdery mildew is one of the most devastating diseases of wheat which significantly decreases yield and quality. Identification of new sources of resistance and their implementation in breeding programs is the most effective way of disease control. Two major powdery mildew resistance loci conferring resistance to all races in seedling and adult plant stages were identified in the emmer wheat landrace GZ1. Their positions, effects, and transferability were verified using two linkage maps (1,510 codominant SNP markers) constructed from two mapping populations (276 lines in total) based on the resistant GZ1 line. The dominant resistance locus QPm.GZ1-7A was located in a 90 cM interval of chromosome 7AL and explains up to 20% of the trait variation. The recessive locus QPm.GZ1-2A, which provides total resistance, explains up to 40% of the trait variation and was located in the distal part of chromosome 2AL. The locus was saturated with 14 PCR-based markers and delimited to a 0.99 cM region which corresponds to 4.3 Mb of the cv. Zavitan reference genome and comprises 55 predicted genes with no apparent candidate for the QPm.GZ1-2A resistance gene. No recessive resistance gene or allele was located at the locus before, suggesting the presence of a new powdery mildew resistance gene in the GZ1. The mapping data and markers could be used for the implementation of the locus in breeding. Moreover, they are an ideal base for cloning and study of host-pathogen interaction pathways determined by the resistance genes.

Improving Quantitative Traits in Self-Pollinated Crops Using Simulation-Based Selection With Minimal Crossing

Genomic selection and marker-assisted recurrent selection have been applied to improve quantitative traits in many cross-pollinated crops. However, such selection is not feasible in self-pollinated crops owing to laborious crossing procedures. In this study, we developed a simulation-based selection strategy that makes use of a trait prediction model based on genomic information to predict the phenotype of the progeny for all possible crossing combinations. These predictions are then used to select the best cross combinations for the selection of the given trait. In our simulated experiment, using a biparental initial population with a heritability set to 0.3, 0.6, or 1.0 and the number of quantitative trait loci set to 30 or 100, the genetic gain of the proposed strategy was higher or equal to that of conventional recurrent selection method in the early selection cycles, although the number of cross combinations of the proposed strategy was considerably reduced in each cycle. Moreover, this strategy was demonstrated to increase or decrease seed protein content in soybean recombinant inbred lines using SNP markers. Information on 29 genomic regions associated with seed protein content was used to construct the prediction model and conduct simulation. After two selection cycles, the selected progeny had significantly higher or lower seed protein contents than those from the initial population. These results suggest that our strategy is effective in obtaining superior progeny over a short period with minimal crossing and has the potential to efficiently improve the target quantitative traits in self-pollinated crops.

Robust regression based genome-wide multi-trait QTL analysis

In genome-wide quantitative trait locus (QTL) mapping studies, multiple quantitative traits are often measured along with the marker genotypes. Multi-trait QTL (MtQTL) analysis, which includes multiple quantitative traits together in a single model, is an efficient technique to increase the power of QTL identification. The two most widely used classical approaches for MtQTL mapping are Gaussian Mixture Model-based MtQTL (GMM-MtQTL) and Linear Regression Model-based MtQTL (LRM-MtQTL) analyses. There are two types of LRM-MtQTL approach known as least squares-based LRM-MtQTL (LS-LRM-MtQTL) and maximum likelihood-based LRM-MtQTL (ML-LRM-MtQTL). These three classical approaches are equivalent alternatives for QTL detection, but ML-LRM-MtQTL is computationally faster than GMM-MtQTL and LS-LRM-MtQTL. However, one major limitation common to all the above classical approaches is that they are very sensitive to outliers, which leads to misleading results. Therefore, in this study, we developed an LRM-based robust MtQTL approach, called LRM-RobMtQTL, for the backcross population based on the robust estimation of regression parameters by maximizing the β-likelihood function induced from the β-divergence with multivariate normal distribution. When β = 0, the proposed LRM-RobMtQTL method reduces to the classical ML-LRM-MtQTL approach. Simulation studies showed that both ML-LRM-MtQTL and LRM-RobMtQTL methods identified the same QTL positions in the absence of outliers. However, in the presence of outliers, only the proposed method was able to identify all the true QTL positions. Real data analysis results revealed that in the presence of outliers only our LRM-RobMtQTL approach can identify all the QTL positions as those identified in the absence of outliers by both methods. We conclude that our proposed LRM-RobMtQTL analysis approach outperforms the classical MtQTL analysis methods.

Hierarchical mixed-model expedites genome-wide longitudinal association analysis

A hierarchical random regression model (Hi-RRM) was extended into a genome-wide association analysis for longitudinal data, which significantly reduced the dimensionality of repeated measurements. The Hi-RRM first modeled the phenotypic trajectory of each individual using a RRM and then associated phenotypic regressions with genetic markers using a multivariate mixed model (mvLMM). By spectral decomposition of genomic relationship and regression covariance matrices, the mvLMM was transformed into a multiple linear regression, which improved computing efficiency while implementing mvLMM associations in efficient mixed-model association expedited (EMMAX). Compared with the existing RRM-based association analyses, the statistical utility of Hi-RRM was demonstrated by simulation experiments. The method proposed here was also applied to find the quantitative trait nucleotides controlling the growth pattern of egg weights in poultry data.

Improving root characterisation for genomic prediction in cassava

Cassava is cultivated due to its drought tolerance and high carbohydrate-containing storage roots. The lack of uniformity and irregular shape of storage roots poses constraints on harvesting and post-harvest processing. Here, we phenotyped the Genetic gain and offspring (C1) populations from the International Institute of Tropical Agriculture (IITA) breeding program using image analysis of storage root photographs taken in the field. In the genome-wide association analysis (GWAS), we detected for most shape and size-related traits, QTL on chromosomes 1 and 12. In a previous study, we found the QTL on chromosome 12 to be associated with cassava mosaic disease (CMD) resistance. Because the root uniformity is important for breeding, we calculated the standard deviation (SD) of individual root measurements per clone. With SD measurements we identified new significant QTL for Perimeter, Feret and Aspect Ratio on chromosomes 6, 9 and 16. Predictive accuracies of root size and shape image-extracted traits were mostly higher than yield trait prediction accuracies. This study aimed to evaluate the feasibility of the image phenotyping protocol and assess GWAS and genomic prediction for size and shape image-extracted traits. The methodology described and the results are promising and open up the opportunity to apply high-throughput methods in cassava.

Variation in phosphorus and sulfur content shapes the genetic architecture and phenotypic associations within the wheat grain ionome

Dissection of the genetic basis of wheat ionome is crucial for understanding the physiological and biochemical processes underlying mineral accumulation in seeds, as well as for efficient crop breeding. Most of the elements essential for plants are metals stored in seeds as chelate complexes with phytic acid or sulfur-containing compounds. We assume that the involvement of phosphorus and sulfur in metal chelation is the reason for strong phenotypic correlations within ionome. Adjustment of element concentrations for the effect of variation in phosphorus and sulfur seed content resulted in drastic change of phenotypic correlations between the elements. The genetic architecture of wheat grain ionome was characterized by quantitative trait loci (QTL) analysis using a cross between durum and wild emmer wheat. QTL analysis of the adjusted traits and two-trait analysis of the initial traits paired with either P or S considerably improved QTL detection power and accuracy, resulting in the identification of 105 QTLs and 617 QTL effects for 11 elements. Candidate gene search revealed some potential functional associations between QTLs and corresponding genes within their intervals. Thus, we have shown that accounting for variation in P and S is crucial for understanding of the physiological and genetic regulation of mineral composition of wheat grain ionome and can be implemented for other plants.

Genome-wide association studies on the phyllosphere microbiome: Embracing complexity in host-microbe interactions

Environmental sequencing shows that plants harbor complex communities of microbes that vary across environments. However, many approaches for mapping plant genetic variation to microbe-related traits were developed in the relatively simple context of binary host-microbe interactions under controlled conditions. Recent advances in sequencing and statistics make genome-wide association studies (GWAS) an increasingly promising approach for identifying the plant genetic variation associated with microbes in a community context. This review discusses early efforts on GWAS of the plant phyllosphere microbiome and the outlook for future studies based on human microbiome GWAS. A workflow for GWAS of the phyllosphere microbiome is then presented, with particular attention to how perspectives on the mechanisms, evolution and environmental dependence of plant-microbe interactions will influence the choice of traits to be mapped.

Novel Resampling Improves Statistical Power for Multiple-Trait QTL Mapping

Multiple-trait analysis typically employs models that associate a quantitative trait locus (QTL) with all of the traits. As a result, statistical power for QTL detection may not be optimal if the QTL contributes to the phenotypic variation in only a small proportion of the traits. Excluding QTL effects that contribute little to the test statistic can improve statistical power. In this article, we show that an optimal power can be achieved when the number of QTL effects is best estimated, and that a stringent criterion for QTL effect selection may improve power when the number of QTL effects is small but can reduce power otherwise. We investigate strategies for excluding trivial QTL effects, and propose a method that improves statistical power when the number of QTL effects is relatively small, and fairly maintains the power when the number of QTL effects is large. The proposed method first uses resampling techniques to determine the number of nontrivial QTL effects, and then selects QTL effects by the backward elimination procedure for significance test. We also propose a method for testing QTL-trait associations that are desired for biological interpretation in applications. We validate our methods using simulations and Arabidopsis thaliana transcript data.

Genome-wide detection of genetic markers associated with growth and fatness in four pig populations using four approaches

BACKGROUND

Genome-wide association studies (GWAS) have been extensively used to identify genomic regions associated with a variety of phenotypic traits in pigs. Until now, most GWAS have explored single-trait association models. Here, we conducted both single- and multi-trait GWAS and a meta-analysis for nine fatness and growth traits on 2004 pigs from four diverse populations, including a White Duroc × Erhualian F₂ intercross population and Chinese Sutai, Laiwu and Erhualian populations.

RESULTS

We identified 44 chromosomal regions that were associated with the nine traits, including four genome-wide significant single nucleotide polymorphisms (SNPs) on SSC2 (SSC for Sus scrofa chromosome), 4, 7 and X. Compared to the single-population GWAS, the meta-analysis was less powerful for the identification of SNPs with population-specific effects but more powerful for the detection of SNPs with population-shared effects. Multiple-trait analysis reduced the power to detect trait-specific SNPs but significantly enhanced the power to identify common SNPs across traits. The SNP on SSC7 had pleiotropic effects on the nine traits in the F₂ and Erhualian populations. Another pleiotropic SNP was observed on SSCX for these traits in the F₂ and Sutai populations. Both population-specific and shared SNPs were identified in this study, thus reflecting the complex genetic architecture of pig growth and fatness traits.

CONCLUSIONS

We demonstrate that the multi-trait method and the meta-analysis on multiple populations can be used to increase the power of GWAS. The two significant SNPs on SSC7 and X had pleiotropic effects in the F₂, Erhualian and Sutai populations.

Pleiotropic Meta-Analyses of Longitudinal Studies Discover Novel Genetic Variants Associated with Age-Related Diseases

Age-related diseases may result from shared biological mechanisms in intrinsic processes of aging. Genetic effects on age-related diseases are often modulated by environmental factors due to their little contribution to fitness or are mediated through certain endophenotypes. Identification of genetic variants with pleiotropic effects on both common complex diseases and endophenotypes may reveal potential conflicting evolutionary pressures and deliver new insights into shared genetic contribution to healthspan and lifespan. Here, we performed pleiotropic meta-analyses of genetic variants using five NIH-funded datasets by integrating univariate summary statistics for age-related diseases and endophenotypes. We investigated three groups of traits: (1) endophenotypes such as blood glucose, blood pressure, lipids, hematocrit, and body mass index, (2) time-to-event outcomes such as the age-at-onset of diabetes mellitus (DM), cancer, cardiovascular diseases (CVDs) and neurodegenerative diseases (NDs), and (3) both combined. In addition to replicating previous findings, we identify seven novel genome-wide significant loci (< 5e-08), out of which five are low-frequency variants. Specifically, from Group 2, we find rs7632505 on 3q21.1 in SEMA5B, rs460976 on 21q22.3 (1 kb from TMPRSS2) and rs12420422 on 11q24.1 predominantly associated with a variety of CVDs, rs4905014 in ITPK1 associated with stroke and heart failure, rs7081476 on 10p12.1 in ANKRD26 associated with multiple diseases including DM, CVDs, and NDs. From Group 3, we find rs8082812 on 18p11.22 and rs1869717 on 4q31.3 associated with both endophenotypes and CVDs. Our follow-up analyses show that rs7632505, rs4905014, and rs8082812 have age-dependent effects on coronary heart disease or stroke. Functional annotation suggests that most of these SNPs are within regulatory regions or DNase clusters and in linkage disequilibrium with expression quantitative trait loci, implying their potential regulatory influence on the expression of nearby genes. Our mediation analyses suggest that the effects of some SNPs are mediated by specific endophenotypes. In conclusion, these findings indicate that loci with pleiotropic effects on age-related disorders tend to be enriched in genes involved in underlying mechanisms potentially related to nervous, cardiovascular and immune system functions, stress resistance, inflammation, ion channels and hematopoiesis, supporting the hypothesis of shared pathological role of infection, and inflammation in chronic age-related diseases.

Explorations in genome-wide association studies and network analyses with dairy cattle fertility traits

The objective of this study was to identify single nucleotide polymorphisms and gene networks associated with 3 fertility traits in dairy cattle-daughter pregnancy rate, heifer conception rate, and cow conception rate-using different approaches. Deregressed predicted transmitting abilities were available for approximately 24,000 Holstein bulls and 36,000 Holstein cows sampled from the National Dairy Database with high-density genotypes. Of those, 1,732 bulls and 375 cows had been genotyped with the Illumina BovineHD Genotyping BeadChip (Illumina Inc., San Diego, CA). The remaining animals were genotyped with various chips of lower density that were imputed to high density. Univariate and trivariate genome-wide association studies (GWAS) with both medium- (60,671 markers) and high-density (312,614 markers) panels were performed for daughter pregnancy rate, heifer conception rate, and cow conception rate using GEMMA (version 0.94; http://www.xzlab.org/software.html). Analyses were conducted using bulls only, cows only, and a sample of both bulls and cows. The partial correlation and information theory algorithm was used to develop gene interaction networks. The most significant markers were further investigated to identify putatively associated genes. Little overlap in associated genes could be found between GWAS using different reference populations of bulls only, cows only, and combined bulls and cows. The partial correlation and information theory algorithm was able to identify several genes that were not identified by ordinary GWAS. The results obtained herein will aid in further dissecting the complex biology underlying fertility traits in dairy cattle, while also providing insight into the nuances of GWAS.

E-Index for Differentiating Complex Dynamic Traits

While it is a daunting challenge in current biology to understand how the underlying network of genes regulates complex dynamic traits, functional mapping, a tool for mapping quantitative trait loci (QTLs) and single nucleotide polymorphisms (SNPs), has been applied in a variety of cases to tackle this challenge. Though useful and powerful, functional mapping performs well only when one or more model parameters are clearly responsible for the developmental trajectory, typically being a logistic curve. Moreover, it does not work when the curves are more complex than that, especially when they are not monotonic. To overcome this inadaptability, we therefore propose a mathematical-biological concept and measurement, E-index (earliness-index), which cumulatively measures the earliness degree to which a variable (or a dynamic trait) increases or decreases its value. Theoretical proofs and simulation studies show that E-index is more general than functional mapping and can be applied to any complex dynamic traits, including those with logistic curves and those with nonmonotonic curves. Meanwhile, E-index vector is proposed as well to capture more subtle differences of developmental patterns.

Early vigour improves phosphate uptake in wheat

Quantitative trait loci (QTLs) for shoot biomass were identified in wheat grown on a soil high in total phosphorus (P) but low in plant-available P. The two populations screened included recombinant inbred lines (RILs) from Chuan-Mai 18/Vigour 18 and doubled-haploid lines from Kukri/Janz. Glasshouse-grown plants were harvested at the five-leaf stage. Seven QTLs for shoot biomass were identified in the RILs, with the largest on chromosome 7A accounting for 7.4% of the phenotypic variance. RILs from the upper tail had larger embryos than RILs from the lower tail. Tail lines were then grown in non-limiting P and the results indicated that early vigour and the capacity to access P contributed to the initial distribution. The influence of early vigour on P nutrition was examined further with advanced vigour lines (AVLs). The AVLs accumulated more shoot biomass, maintained lower shoot P concentrations, and showed greater P-acquisition efficiency than Vigour 18. Nine QTLs for shoot biomass were identified in the Kukri/Janz population. Two on chromosomes 4B and 4D accounted for 24.8% of the variance. Candidates underlying these QTLs are the Rht genes. We confirmed the influence of these genes using near-isogenic lines with different Rht alleles. The dwarf and semi-dwarf alleles affected shoot and root biomass at high and low P but not the efficiency of P acquisition. We conclude that early vigour contributed to the distributions in both populations. Early vigour can increase plant growth at suboptimal P and some sources can also improve the efficiency of P acquisition.

Genome-Wide Association Analysis of Adaptation Using Environmentally Predicted Traits

Current methods for studying the genetic basis of adaptation evaluate genetic associations with ecologically relevant traits or single environmental variables, under the implicit assumption that natural selection imposes correlations between phenotypes, environments and genotypes. In practice, observed trait and environmental data are manifestations of unknown selective forces and are only indirectly associated with adaptive genetic variation. In theory, improved estimation of these forces could enable more powerful detection of loci under selection. Here we present an approach in which we approximate adaptive variation by modeling phenotypes as a function of the environment and using the predicted trait in multivariate and univariate genome-wide association analysis (GWAS). Based on computer simulations and published flowering time data from the model plant Arabidopsis thaliana, we find that environmentally predicted traits lead to higher recovery of functional loci in multivariate GWAS and are more strongly correlated to allele frequencies at adaptive loci than individual environmental variables. Our results provide an example of the use of environmental data to obtain independent and meaningful information on adaptive genetic variation.

Finding genes involved in adaptation to the environment has long been of interest to evolutionary biologists and ecologists. Most commonly, researchers look for loci whose differences in allelic state correlate with differences in a particular trait or environmental variable such as temperature. The implicit assumption behind such methods is that natural selection by the environment will shape variation in adaptive traits through associated changes in allele frequencies. This means that both environmental and phenotypic variation are relevant for detecting adaptive genes, although we have incomplete knowledge of how the two types of variation relate to adaptation. Here we present a method that aims to identify adaptive genes by combining phenotypic and environmental data. We first predict trait variation from a set of environmental variables as a way to extract the most biologically relevant information from the environment and then look for genes associated with both the predicted and observed trait. Using simulations and published data from the model plant Arabidopsis thaliana, we show that this approach may find adaptive genes more effectively compared to existing methods. We also demonstrate that predicted traits can be used to identify relevant loci in individuals for which no phenotypic data is available.

Efficient multiple-trait association and estimation of genetic correlation using the matrix-variate linear mixed model

Multiple-trait association mapping, in which multiple traits are used simultaneously in the identification of genetic variants affecting those traits, has recently attracted interest. One class of approaches for this problem builds on classical variance component methodology, utilizing a multitrait version of a linear mixed model. These approaches both increase power and provide insights into the genetic architecture of multiple traits. In particular, it is possible to estimate the genetic correlation, which is a measure of the portion of the total correlation between traits that is due to additive genetic effects. Unfortunately, the practical utility of these methods is limited since they are computationally intractable for large sample sizes. In this article, we introduce a reformulation of the multiple-trait association mapping approach by defining the matrix-variate linear mixed model. Our approach reduces the computational time necessary to perform maximum-likelihood inference in a multiple-trait model by utilizing a data transformation. By utilizing a well-studied human cohort, we show that our approach provides more than a 10-fold speedup, making multiple-trait association feasible in a large population cohort on the genome-wide scale. We take advantage of the efficiency of our approach to analyze gene expression data. By decomposing gene coexpression into a genetic and environmental component, we show that our method provides fundamental insights into the nature of coexpressed genes. An implementation of this method is available at http://genetics.cs.ucla.edu/mvLMM.

Two-stage genome-wide search for epistasis with implementation to Recombinant Inbred Lines (RIL) populations

OBJECTIVE AND METHODS

This paper proposes an inegrative two-stage genome-wide search for pairwise epistasis on expression quantitative trait loci (eQTL). The traits are clustered into multi-trait complexes that account for correlations between them that may result from common epistasis effects. The search is done by first screening for epistatic regions and then using dense markers within the identified regions, resulting in substantial reduction in the number of tests for epistasis. The FDR is controlled using a hierarchical procedure that accounts for the search structure. Each combination of trait and marker-pair is tested using a model that accounts for both statistical and functional interpretations of epistasis and considers orthogonal effects, such that their contributions to heritability can be estimated individually. We examine the impact of using multi-trait complexes rather than single traits, and of using a hierarchical search for epistasis rather than skipping the initial screen for epistatic regions. We apply the proposed algorithm on Arabidopsis transcription data.

PRINCIPAL FINDINGS

Both epistasis detection power and heritability contributed by epistasis increased when using multi-trait complexes rather than single traits. Epistatic effects common to the eQTLs included in the complexes have higher chance of being identified by analysis of multi-trait complexes, particularly when epistatic effects on individual traits are small. Compared to direct testing for all potential epistatic effects, the hierarchical search was substantially more powerful in detecting epistasis, while controlling the FDR at the desired level. Association in functional roles within genomic regions was observed, supporting an initial screen for epistatic QTLs.

Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix

Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate independent 'super traits' from the original multivariate phenotypic traits for the univariate analysis. However, parameter estimates in this framework may not be the same as those from the joint analysis of all traits, leading to spurious linkage results. In this paper, we propose to perform the PCA for residual covariance matrix instead of the phenotypical covariance matrix, based on which multiple traits are transformed to a group of pseudo principal components. The PCA for residual covariance matrix allows analyzing each pseudo principal component separately. In addition, all parameter estimates are equivalent to those obtained from the joint multivariate analysis under a linear transformation. However, a fast least absolute shrinkage and selection operator (LASSO) for estimating the sparse oversaturated genetic model greatly reduces the computational costs of this procedure. Extensive simulations show statistical and computational efficiencies of the proposed method. We illustrate this method in a GWAS for 20 slaughtering traits and meat quality traits in beef cattle.

TraitCapture: genomic and environment modelling of plant phenomic data

Agriculture requires a second green revolution to provide increased food, fodder, fiber, fuel and soil fertility for a growing population while being more resilient to extreme weather on finite land, water, and nutrient resources. Advances in phenomics, genomics and environmental control/sensing can now be used to directly select yield and resilience traits from large collections of germplasm if software can integrate among the technologies. Traits could be Captured throughout development and across environments from multi-dimensional phenotypes, by applying Genome Wide Association Studies (GWAS) to identify causal genes and background variation and functional structural plant models (FSPMs) to predict plant growth and reproduction in target environments. TraitCapture should be applicable to both controlled and field environments and would allow breeders to simulate regional variety trials to pre-select for increased productivity under challenging environments.

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.

We describe novel methods for finding significant associations between a genome wide panel of SNPs and multiple complex traits, and further for distinguishing between genes with effects on multiple traits and multiple linked genes affecting different traits. The method uses a meta-analysis based on estimates of SNP effects from independent single trait genome wide association studies (GWAS). The method could therefore be widely used to combine already published GWAS results. The method was applied to 32 traits that describe growth, body composition, feed intake and reproduction in 10,191 beef cattle genotyped for approximately 700,000 SNP. The genes found to be associated with these traits can be arranged into 4 groups that differ in their pattern of effects and hence presumably in their physiological mechanism of action. For instance, one group of genes affects weight and fatness in the opposite direction and can be described as a group of genes affecting mature size, while another group affects weight and fatness in the same direction.

QTLs influencing IGF-1 levels in a LOU/CxFischer 344F2 rat population. Tracks towards the metabolic theory of Ageing

OBJECTIVE

Since a reduction of the insulin/IGF-1 signaling cascade extends life span in many species and IGF-1 signaling might partly mediate the effects of caloric restriction (CR), an experimental intervention for increasing longevity, the purpose of the present study was to use quantitative trait loci (QTL) analysis, an unbiased genetic approach, to identify particular regions of the genome influencing plasma IGF-1 levels in an F2 intercross between F344 and LOU/C rats; the latter being an inbred strain of Wistar origin, considered as a model of healthy aging since it resists to age (and diet)-induced obesity.

DESIGN

F1 hybrids were obtained by crossbreeding LOU/C with F344 rats, and then F1 were bred inter se to obtain the F2 population, of which 93 males and 94 females were studied. Total plasma IGF-1 levels were determined by radioimmunoassay. A genome scan of the F2 population was made with 100 microsatellite markers) selected for their polymorphism between LOU/C and F344 strains (and by covering evenly the whole genome.

RESULTS

By simple interval mapping sex-dependent QTLs were found on chromosome 17 in males and on chromosome 18 in females. By multiple interval mapping, additional QTLs were found on chromosomes 1, 4, 5, 6, 12, 15 and 19 in males and on chromosomes 3, 5, 6, 12 and 17 in females. Only the markers D1Rat196 and D12Mgh5 were found in both males and females. The majority of QTLs corresponded to metabolic syndrome (cardiac function: n = 45 (30%), obesity/diabetes: n = 22 (15%), inflammation: n = 19 (13%) and only a limited number to body weight: n = 13 (9%), proliferation (n = 10 (7%) or ossification: n = 7 (5%). Ninety-six candidate genes were located on the different QTLs. A significant proportion of these genes are connected to IGF-1 production and receptor pathways (n = 18) or metabolic syndrome (n = 11).

CONCLUSIONS

Subsequent studies are necessary to determine whether the genetic networks underscored are also involved in age-associated obesity, diabetes and inflammation as well as cardiovascular impairments.

Bivariate genome-wide association analysis of the growth and intake components of feed efficiency

Single nucleotide polymorphisms (SNPs) associated with average daily gain (ADG) and dry matter intake (DMI), two major components of feed efficiency in cattle, were identified in a genome-wide association study (GWAS). Uni- and multi-SNP models were used to describe feed efficiency in a training data set and the results were confirmed in a validation data set. Results from the univariate and bivariate analyses of ADG and DMI, adjusted by the feedlot beef steer maintenance requirements, were compared. The bivariate uni-SNP analysis identified (P-value <0.0001) 11 SNPs, meanwhile the univariate analyses of ADG and DMI identified 8 and 9 SNPs, respectively. Among the six SNPs confirmed in the validation data set, five SNPs were mapped to KDELC2, PHOX2A, and TMEM40. Findings from the uni-SNP models were used to develop highly accurate predictive multi-SNP models in the training data set. Despite the substantially smaller size of the validation data set, the training multi-SNP models had slightly lower predictive ability when applied to the validation data set. Six Gene Ontology molecular functions related to ion transport activity were enriched (P-value <0.001) among the genes associated with the detected SNPs. The findings from this study demonstrate the complementary value of the uni- and multi-SNP models, and univariate and bivariate GWAS analyses. The identified SNPs can be used for genome-enabled improvement of feed efficiency in feedlot beef cattle, and can aid in the design of empirical studies to further confirm the associations.

An efficient approach to large-scale genotype-phenotype association analyses

Modern molecular biotechnology generates a great deal of intermediate information, such as transcriptional and metabolic products in bridging DNA and complex traits. In genome-wide linkage analysis and genome-wide association study, regression analysis for large-scale correlated phenotypes is applied to map genes for those by-products that are regarded as quantitative traits. For a single trait, least absolute shrinkage and selection operator with coordinate descent step can be employed to efficiently shrink sparse non-zero genetic effects of quantitative trait loci (QTLs). However, regression analyses in a trait-by-trait basis do not take account of the correlations among the analyzed traits. In this study, conditional phenotype of each trait is defined, given other traits. Large-scale genotype-phenotype association analyses are therefore transformed to separate genotype-conditional phenotype ones. Meanwhile, the correlation architecture between each trait and other traits can also be provided by shrinkage estimation for each conditional phenotype. Simulation demonstrates that the proposed conditional mapping method is generally identical to joint mapping method based on multivariate analysis in terms of statistical detection power and parameter estimation. Application of the method is provided to locate eQTL in yeast.

Evolution of an adaptive behavior and its sensory receptors promotes eye regression in blind cavefish: response to Borowsky (2013)

Vibration attraction behavior (VAB) is the swimming of fish toward an oscillating object, a behavior that is likely adaptive because it increases foraging efficiency in darkness. VAB is seen in a small proportion of Astyanax surface-dwelling populations (surface fish) but is pronounced in cave-dwelling populations (cavefish). In a recent study, we identified two quantitative trait loci for VAB on Astyanax linkage groups 2 and 17. We also demonstrated that a small population of superficial neuromast sensors located within the eye orbit (EO SN) facilitate VAB, and two quantitative trait loci (QTL) were identified for EO SN that were congruent with those for VAB. Finally, we showed that both VAB and EO SN are negatively correlated with eye size, and that two (of several) QTL for eye size overlap VAB and EO SN QTLs. From these results, we concluded that the adaptive evolution of VAB and EO SN has contributed to the indirect loss of eyes in cavefish, either as a result of pleiotropy or tight physical linkage of the mutations underlying these traits. In a subsequent commentary, Borowsky argues that there is poor experimental support for our conclusions. Specifically, Borowsky states that: (1) linkage groups (LGs) 2 and 17 harbor QTL for many traits and, therefore, no evidence exists for an exclusive interaction among the overlapping VAB, EO SN and eye size QTL; (2) some of the QTL we identified are too broad (>20 cM) to support the hypothesis of correlated evolution due to pleiotropy or hitchhiking; and (3) VAB is unnecessary to explain the indirect evolution of eye-loss since the negative polarity of numerous eye QTL is consistent with direct selection against eyes. Borowsky further argues that (4) it is difficult to envision an evolutionary scenario whereby VAB and EO SN drive eye loss, since the eyes must first be reduced in order to increase the number of EO SN and, therefore, VAB. In this response, we explain why the evidence of one trait influencing eye reduction is stronger for VAB than other traits, and provide further support for a scenario whereby elaboration of VAB in surface fish may precede complete eye-loss.

QTL dissection of the loss of green colour during post-anthesis grain maturation in two-rowed barley

Ability to genetically manipulate the loss of green colour during grain maturation has potentials for increasing productivity, disease resistance, and drought and heat tolerance in crop plants. Two doubled haploid, two-rowed barley populations (Vlamingh × Buloke and VB9524 × ND11231*12) were monitored over 2 years for loss of green colour during grain filling using a portable active sensor. The aims were to determine the genomic regions that control trait heritability by quantitative trait locus (QTL) analysis, and to examine patterns of QTL-environment interactions under different conditions of water stress. In the Vlamingh × Buloke cross, broad-sense heritability estimate for loss of green colour (measured as the difference in sensor readings taken at anthesis and maturity, ∆SRI) was 0.68, and 0.78 for the VB9524 × ND11231*12 population. In the VB9524 × ND11231*12 population, rapid loss of green colour was positively associated with grain yield and percent plump grains, but in the Vlamingh × Buloke population, a slower loss of green colour (low ∆SRI) was associated with increased grain plumpness. With the aid of a dense array of single nucleotide polymorphisms (SNPs) and EST-derived SSR markers, a total of nine QTLs were detected across the two populations. Of these, a single major locus on the short arm of barley chromosome 5H was consistently linked with trait variation across the populations and multiple environments. The QTL was independent of flowering time and explained between 5.4 and 15.4 % of the variation observed in both populations, depending on the environment, and although a QTL × E interaction was detected, it was largely due to a change in the magnitude of the effect, rather than a change in direction. The results suggest that loss of green colour during grain maturation may be under the control of a simple genetic architecture, but a careful study of target populations and environments would be required for breeding purposes.

QTL affecting fitness of hybrids between wild and cultivated soybeans in experimental fields

The objective of this study was to identify quantitative trait loci (QTL) affecting fitness of hybrids between wild soybean (Glycine soja) and cultivated soybean (Glycine max). Seed dormancy and seed number, both of which are important for fitness, were evaluated by testing artificial hybrids of G. soja × G. max in a multiple-site field trial. Generally, the fitness of the F₁ hybrids and hybrid derivatives from self-pollination was lower than that of G. soja due to loss of seed dormancy, whereas the fitness of hybrid derivatives with higher proportions of G. soja genetic background was comparable with that of G. soja. These differences were genetically dissected into QTL for each population. Three QTLs for seed dormancy and one QTL for total seed number were detected in the F₂ progenies of two diverse cross combinations. At those four QTLs, the G. max alleles reduced seed number and severely reduced seed survival during the winter, suggesting that major genes acquired during soybean adaptation to cultivation have a selective disadvantage in natural habitats. In progenies with a higher proportion of G. soja genetic background, the genetic effects of the G. max alleles were not expressed as phenotypes because the G. soja alleles were dominant over the G. max alleles. Considering the highly inbreeding nature of these species, most hybrid derivatives would disappear quickly in early self-pollinating generations in natural habitats because of the low fitness of plants carrying G. max alleles.

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%).

Moving toward System Genetics through Multiple Trait Analysis in Genome-Wide Association Studies

Association studies are a staple of genotype–phenotype mapping studies, whether they are based on single markers, haplotypes, candidate genes, genome-wide genotypes, or whole genome sequences. Although genetic epidemiological studies typically contain data collected on multiple traits which themselves are often correlated, most analyses have been performed on single traits. Here, I review several methods that have been developed to perform multiple trait analysis. These methods range from traditional multivariate models for systems of equations to recently developed graphical approaches based on network theory. The application of network theory to genetics is termed systems genetics and has the potential to address long-standing questions in genetics about complex processes such as coordinate regulation, homeostasis, and pleiotropy.

Second-generation high-throughput forward genetic screen in mice to isolate subtle behavioral mutants

Forward genetic screens have been highly successful in revealing roles of genes and pathways in complex biological events. Traditionally these screens have focused on isolating mutants with the greatest phenotypic deviance, with the hopes of discovering genes that are central to the biological event being investigated. Behavioral screens in mice typically use simple activity-based assays as endophenotypes for more complex emotional states of the animal. They generally set the selection threshold for a putative mutant at 3 SDs (z score of 3) from the average behavior of normal animals to minimize false-positive results. Behavioral screens using a high threshold for detection have generally had limited success, with high false-positive rates and subtle phenotypic differences that have made mapping and cloning difficult. In addition, targeted reverse genetic approaches have shown that when genes central to behaviors such as open field behavior, psychostimulant response, and learning and memory tasks are mutated, they produce subtle phenotypes that differ from wild-type animals by 1 to 2 SDs (z scores of 1 to 2). We have conducted a second-generation (G2) dominant N-ethyl-N-nitrosourea (ENU) screen especially designed to detect subtle behavioral mutants for open field activity and psychostimulant response behaviors. We successfully detect mutant lines with only 1 to 2 SD shifts in mean response compared with wild-type control animals and present a robust statistical and methodological framework for conducting such forward genetic screens. Using this methodology we have screened 229 ENU mutant lines and have identified 15 heritable mutant lines. We conclude that for screens in mice that use activity-based endophenotypic measurements for complex behavioral states, this G2 screening approach yields better results.

Expression of trypanotolerance in N'Dama x Boran crosses under field challenge in relation to N'Dama genome content

Background

Animal trypanosomosis in sub-Saharan Africa is a major obstacle to livestock based agriculture. Control relies on drugs with increasing incidence of multiple-drug resistance. A previous mapping experiment in an F2 population derived from the indigenous trypanotolerant N’Dama cattle crossed to susceptible (Kenya)-Boran cattle under controlled challenge, uncovered a number of trypanotolerance QTL (T-QTL). The present study was to determine expression of N’Dama trypanotolerance in a backcross to the Boran under conditions of field challenge, and whether chromosomal regions associated with trypanotolerance in the F2 experiment showed similar effects in the BC population.

Methods

192 backcross animals to the Boran were produced in six batches from June 2001 to December 2006. At one year of age animals were moved to the field and exposed to natural challenge over about one year in Southwest Kenya (Narok). The animals were individually recorded weekly for body weight, packed cell volume, parasitaemia score, and drug treatments, and were genotyped using 35 microsatellite markers spanning 5 chromosomes found in the F2 study to harbour T-QTL.

Results

The F1 were most trypanotolerant, Boran least, and BC intermediate. Females showed distinctly higher trypanotolerance than males. There was a positive correlation in the BC population between trypanotolerance and number of N’Dama origin marker alleles. QTL mapping revealed T-QTL distributed among all five targeted chromosomes, corresponding in part to the results obtained in the F₂ experiment.

Conclusions

N’Dama origin trypanotolerance is expressed in a BC population under field conditions in proportion to N’Dama origin marker alleles. Consequently, marker assisted selection in such populations may be a means of increasing trypanotolerance, while retaining the desirable productive qualities of the recurrent parent.

Multivariate analysis of a genome-wide association study in dairy cattle

Multiple-trait genome-wide association study (GWAS) analyses were compared with single-trait GWAS for power to discover and subsequently validate genetic markers (single nucleotide polymorphisms; SNP) associated with dairy traits. The SNP associations were discovered in 1 Holstein population and validated in both a Holstein population consisting of bulls younger than those in the discovery population and a Jersey population. The multivariate methods used were a principal component analysis and a series of bivariate analyses. The statistical power of detecting associations using multiple-trait GWAS was as good as or better than that of the best single-trait GWAS. Additional SNP associations were found with the multivariate methods that had not been discovered in the single-trait analyses; this was achieved without an increase in the false discovery rate. From the multivariate analysis, 4 common pleiotropic patterns were identified among the putative quantitative trait loci (QTL) affecting the Australian selection index. These patterns could be interpreted as a primary effect of the putative QTL on 1 or more milk components and secondary effects on other components. The multivariate analysis did not appear to increase the precision with which putative QTL were mapped.

Genetic control over silica deposition in wheat awns

Awns are long, stiff filamentous extensions of glumes in many grasses. In wheat, awns contribute up to 40% of the grain's photosynthetic assimilates, and assist in seed dispersal. Awns accumulate silica in epidermal hairs and papillae, and silica has been positively associated with yield and environmental stress tolerance. Here, the awns of a set of domesticated wheat genotypes and their direct progenitor, Triticum turgidum ssp. dicoccoides were characterized. In addition, the silica concentration in awns was genetically dissected in a tetraploid wheat population of recombinant inbred lines (RILs) derived from a cross between durum wheat (cv. Langdon) and wild emmer (accession G18-16). Scanning electron micrographs revealed a continuous silica layer under the cuticle. Extended silicification was identified in the epidermis cell wall and in sclerenchyma cells near the vascular bundles, but not in the stomata, suggesting that an active process directs the soluble silica away from the water evaporation stream. The number of silicified cells was linearly correlated to silica concentration in dry weight (DW), suggesting cellular control over silicification. Domesticated wheat awns contained up to 19% silica per DW, as compared with 7% in the wild accessions, suggesting selection pressure associated with the domestication process. Six quantitative trait loci (QTLs) for silica were identified in the awns, with a LOD score of 3.7-6.3, three of which overlapped genomic regions that contribute to high grain protein. Localization of silica in the awns and identification of QTLs help illuminate mechanisms associated with silica metabolism in wheat.

Combining QTL mapping and transcriptome profiling of bulked RILs for identification of functional polymorphism for salt tolerance genes in rice (Oryza sativa L.)

Identification of genes for quantitative traits is difficult using any single approach due to complex inheritance of the traits and limited resolving power of the individual techniques. Here a combination of genetic mapping and bulked transcriptome profiling was used to narrow down the number of differentially expressed salt-responsive genes in rice in order to identify functional polymorphism of genes underlying the quantitative trait loci (QTL). A population of recombinant inbred lines (RILs) derived from cross between salt-tolerant variety CSR 27 and salt-sensitive variety MI 48 was used to map QTL for salt ion concentrations in different tissues and salt stress susceptibility index (SSI) for spikelet fertility, grain weight, and grain yield. Eight significant QTL intervals were mapped on chromosomes 1, 8, and 12 for the salt ion concentrations and a QTL controlling SSI for spikelet fertility was co-located in one of these intervals on chromosome 8. However, there were total 2,681 genes in these QTL intervals, making it difficult to pinpoint the genes responsible for the functional differences for the traits. Similarly, transcriptome profiling of the seedlings of tolerant and sensitive parents grown under control and salt-stress conditions showed 798 and 2,407 differentially expressed gene probes, respectively. By analyzing pools of RNA extracted from ten each of extremely tolerant and extremely sensitive RILs to normalize the background noise, the number of differentially expressed genes under salt stress was drastically reduced to 30 only. Two of these genes, an integral transmembrane protein DUF6 and a cation chloride cotransporter, were not only co-located in the QTL intervals but also showed the expected distortion of allele frequencies in the extreme tolerant and sensitive RILs, and therefore are suitable for future validation studies and development of functional markers for salt tolerance in rice to facilitate marker-assisted breeding.

Bayesian shrinkage mapping for multiple QTL in half-sib families

Recently, an effective Bayesian shrinkage estimation method has been proposed for mapping QTL in inbred line crosses. However, with regard to outbred populations, such as half-sib populations with maternal information unavailable, it is not straightforward to utilize such a shrinkage estimation for QTL mapping. The reasons are: (1) the linkage phase of markers in the outbred population is usually unknown; and (2) only paternal genotypes can be used for inferring QTL genotypes of offspring. In this article, a novel Bayesian shrinkage method was proposed for mapping QTL under the half-sib design using a mixed model. A simulation study clearly demonstrated that the proposed method was powerful for detecting multiple QTL. In addition, we applied the proposed method to map QTL for economic traits in the Chinese dairy cattle population. Two or more novel QTL harbored in the chromosomal region were detected for each trait of interest, whereas only one QTL was found using traditional maximum likelihood analyses in our earlier studies. This further validated that our shrinkage estimation method could perform well in empirical data analyses and had practical significance in the field of linkage studies for outbred populations.

Genomic dissection of drought resistance in durum wheat x wild emmer wheat recombinant inbreed line population

Drought is the major factor limiting wheat productivity worldwide. The gene pool of wild emmer wheat, Triticum turgidum ssp. dicoccoides, harbours a rich allelic repertoire for morpho-physiological traits conferring drought resistance. The genetic and physiological bases of drought responses were studied here in a tetraploid wheat population of 152 recombinant inbreed lines (RILs), derived from a cross between durum wheat (cv. Langdon) and wild emmer (acc# G18-16), under contrasting water availabilities. Wide genetic variation was found among RILs for all studied traits. A total of 110 quantitative trait loci (QTLs) were mapped for 11 traits, with LOD score range of 3.0-35.4. Several QTLs showed environmental specificity, accounting for productivity and related traits under water-limited (20 QTLs) or well-watered conditions (15 QTLs), and in terms of drought susceptibility index (22 QTLs). Major genomic regions controlling productivity and related traits were identified on chromosomes 2B, 4A, 5A and 7B. QTLs for productivity were associated with QTLs for drought-adaptive traits, suggesting the involvement of several strategies in wheat adaptation to drought stress. Fifteen pairs of QTLs for the same trait were mapped to seemingly homoeologous positions, reflecting synteny between the A and B genomes. The identified QTLs may facilitate the use of wild alleles for improvement of drought resistance in elite wheat cultivars.

Mapping QTL for multiple traits using Bayesian statistics

The value of a new crop species is usually judged by the overall performance of multiple traits. Therefore, in most quantitative trait locus (QTL) mapping experiments, researchers tend to collect phenotypic records for multiple traits. Some traits may vary continuously and others may vary in a discrete fashion. Although mapping QTLs jointly for multiple traits is more efficient than mapping QTLs separately for individual traits, the latter is still commonly practised in QTL mapping. This is primarily due to the lack of efficient statistical methods and computer software packages to implement the methods. Mapping multiple QTLs simultaneously in a single multivariate model has not been available, especially when categorical traits are involved. In the present study, we developed a Bayesian method to map QTLs of the entire genome for multiple traits with continuous, discrete or both types of phenotypic distribution. Instead of using the reversible jump Markov chain Monte Carlo (MCMC) for model selection, we adopt a parameter shrinkage approach to estimate the genetic effects of all marker intervals. We demonstrate the method by analysing a set of simulated data with both continuous and discrete traits. We also apply the method to mapping QTLs responsible for multiple disease resistances to the blast fungus of rice. A computer program written in SAS/IML that implements the method is freely available, on request, to academic researchers.

Five QTL hotspots for yield in short rotation coppice bioenergy poplar: the Poplar Biomass Loci

BACKGROUND

Concern over land use for non-food bioenergy crops requires breeding programmes that focus on producing biomass on the minimum amount of land that is economically-viable. To achieve this, the maximum potential yield per hectare is a key target for improvement. For long lived tree species, such as poplar, this requires an understanding of the traits that contribute to biomass production and their genetic control. An important aspect of this for long lived plants is an understanding of genetic interactions at different developmental stages, i.e. how genes or genetic regions impact on yield over time.

RESULTS

QTL mapping identified regions of genetic control for biomass yield. We mapped consistent QTL across multiple coppice cycles and identified five robust QTL hotspots on linkage groups III, IV, X, XIV and XIX, calling these 'Poplar Biomass Loci' (PBL 1-5). In total 20% of the variation in final harvest biomass yield was explained by mapped QTL. We also investigated the genetic correlations between yield related traits to identify 'early diagnostic' indicators of yield showing that early biomass was a reasonable predictor of coppice yield and that leaf size, cell number and stem and sylleptic branch number were also valuable traits.

CONCLUSION

These findings provide insight into the genetic control of biomass production and correlation to 'early diagnostic' traits determining yield in poplar SRC for bioenergy. QTL hotspots serve as useful targets for directed breeding for improved biomass productivity that may also be relevant across additional poplar hybrids.

A comprehensive analysis of QTL for abdominal fat and breast muscle weights on chicken chromosome 5 using a multivariate approach

Quantitative trait loci (QTL) influencing the weight of abdominal fat (AF) and of breast muscle (BM) were detected on chicken chromosome 5 (GGA5) using two successive F(2) crosses between two divergently selected 'Fat' and 'Lean' INRA broiler lines. Based on these results, the aim of the present study was to identify the number, location and effects of these putative QTL by performing multitrait and multi-QTL analyses of the whole available data set. Data concerned 1186 F(2) offspring produced by 10 F(1) sires and 85 F(1) dams. AF and BM traits were measured on F(2) animals at slaughter, at 8 (first cross) or 9 (second cross) weeks of age. The F(0), F(1) and F(2) birds were genotyped for 11 microsatellite markers evenly spaced along GGA5. Before QTL detection, phenotypes were adjusted for the fixed effects of sex, F(2) design, hatching group within the design, and for body weight as a covariable. Univariate analyses confirmed the QTL segregation for AF and BM on GGA5 in male offspring, but not in female offspring. Analyses of male offspring data using multitrait and linked-QTL models led us to conclude the presence of two QTL on the distal part of GGA5, each controlling one trait. Linked QTL models were applied after correction of phenotypic values for the effects of these distal QTL. Several QTL for AF and BM were then discovered in the central region of GGA5, splitting one large QTL region for AF into several distinct QTL. Neither the 'Fat' nor the 'Lean' line appeared to be fixed for any QTL genotype. These results have important implications for prospective fine mapping studies and for the identification of underlying genes and causal mutations.

Multitrait analysis of quantitative trait loci using Bayesian composite space approach

Background

Multitrait analysis of quantitative trait loci can capture the maximum information of experiment. The maximum-likelihood approach and the least-square approach have been developed to jointly analyze multiple traits, but it is difficult for them to include multiple QTL simultaneously into one model.

Results

In this article, we have successfully extended Bayesian composite space approach, which is an efficient model selection method that can easily handle multiple QTL, to multitrait mapping of QTL. There are many statistical innovations of the proposed method compared with Bayesian single trait analysis. The first is that the parameters for all traits are updated jointly by vector or matrix; secondly, for QTL in the same interval that control different traits, the correlation between QTL genotypes is taken into account; thirdly, the information about the relationship of residual error between the traits is also made good use of. The superiority of the new method over separate analysis was demonstrated by both simulated and real data. The computing program was written in FORTRAN and it can be available for request.

Conclusion

The results suggest that the developed new method is more powerful than separate analysis.

Dissection of the genetic architecture of body weight in chicken reveals the impact of epistasis on domestication traits

In this contribution, we study the genetic mechanisms leading to differences in the observed growth patterns of domesticated White Leghorn chickens and their wild ancestor the red jungle fowl. An epistatic QTL analysis for several body-weight measures from hatch to adulthood confirms earlier findings that polymorphisms at >15 loci contribute to body-weight determination in an F(2) intercross between these populations and that many loci are involved in complex genetic interactions. Here, we use a new genetic model to decompose the genetic effects of this multilocus epistatic genetic network. The results show how the functional modeling of genetic effects provides new insights into how genetic interactions in a large set of loci jointly contribute to phenotypic expression. By exploring the functional effects of QTL alleles, we show that some alleles can display temporal shifts in the expression of genetic effects due to their dependencies on the genetic background. Our results demonstrate that the effects of many genes are dependent on genetic interactions with other loci and how their involvement in the domestication process relies on these interactions.

Trait-trait dynamic interaction: 2D-trait eQTL mapping for genetic variation study

Background

Many studies have shown that the abundance level of gene expression is heritable. Analogous to the traditional genetic study, most researchers treat the expression of one gene as a quantitative trait and map it to expression quantitative trait loci (eQTL). This is 1D-trait mapping. 1D-trait mapping ignores the trait-trait interaction completely, which is a major shortcoming.

Results

To overcome this limitation, we study the expression of a pair of genes and treat the variation in their co-expression pattern as a two dimensional quantitative trait. We develop a method to find gene pairs, whose co-expression patterns, including both signs and strengths, are mediated by genetic variations and map these 2D-traits to the corresponding genetic loci. We report several applications by combining 1D-trait mapping with 2D-trait mapping, including the contribution of genetic variations to the perturbations in the regulatory mechanisms of yeast metabolic pathways.

Conclusion

Our approach of 2D-trait mapping provides a novel and effective way to connect the genetic variation with higher order biological modules via gene expression profiles.

A conceptual framework for mapping quantitative trait Loci regulating ontogenetic allometry

Although ontogenetic changes in body shape and its associated allometry has been studied for over a century, essentially nothing is known about their underlying genetic and developmental mechanisms. One of the reasons for this ignorance is the unavailability of a conceptual framework to formulate the experimental design for data collection and statistical models for data analyses. We developed a framework model for unraveling the genetic machinery for ontogenetic changes of allometry. The model incorporates the mathematical aspects of ontogenetic growth and allometry into a maximum likelihood framework for quantitative trait locus (QTL) mapping. As a quantitative platform, the model allows for the testing of a number of biologically meaningful hypotheses to explore the pleiotropic basis of the QTL that regulate ontogeny and allometry. Simulation studies and real data analysis of a live example in soybean have been performed to investigate the statistical behavior of the model and validate its practical utilization. The statistical model proposed will help to study the genetic architecture of complex phenotypes and, therefore, gain better insights into the mechanistic regulation for developmental patterns and processes in organisms.

Linked and pleiotropic QTLs influencing carcass composition traits detected on porcine chromosome 7

A multivariate QTL detection was carried out on fatness and carcass composition traits on porcine chromosome 7 (SSC7). Single-trait QTLs have already been detected in the SLA region, and multivariate approaches have been used to exploit the correlations between the traits to obtain more information on their pattern: almost 500 measurements were recorded for backfat thickness (BFT1, BFT2), backfat weight (BFW) and leaf fat weight (LFW) but only about half that number for intramuscular fat content (IMF), affecting the detection. First, groups of traits were selected using a backward selection procedure: traits were selected based on their contribution to the linear combination of traits discriminating the putative QTL haplotypes. Three groups of traits could be distinguished based on successive discriminant analyses: external fat (BFT1, BFT2), internal fat (LFW, IMF) and BFW. At least four regions were distinguished, preferentially affecting one or the other group, with the SLA region always influencing all the traits. Meishan alleles decreased all trait values except IMF, confirming an opportunity for marker-assisted selection to improve meat quality with maintenance of carcass composition based on Meishan alleles.

Natural and artificially induced genetic variability in crop and model plant species for plant systems biology

The sequencing of plant genomes which was completed a few years ago for Arabidopsis thaliana and Oryza sativa is currently underway for numerous crop plants of commercial value such as maize, poplar, tomato grape or tobacco. In addition, hundreds of thousands of expressed sequence tags (ESTs) are publicly available that may well represent 40-60% of the genes present in plant genomes. Despite its importance for life sciences, genome information is only an initial step towards understanding gene function (functional genomics) and deciphering the complex relationships between individual genes in the framework of gene networks. In this chapter we introduce and discuss means of generating and identifying genetic diversity, i.e., means to genetically perturb a biological system and to subsequently analyse the systems response, e.g., the changes in plant morphology and chemical composition. Generating and identifying genetic diversity is in its own right a highly powerful resource of information and is established as an invaluable tool for systems biology.

The genetic relationship among plant-height traits found using multiple-trait QTL mapping of a dent corn and popcorn cross

Plant height (PH) is one of the most important traits in maize breeding programs. In popcorn, inferior plant traits can be improved with the dent/flint corn germplasm. In the current study, a total of 259 F2:3families, developed from a cross between a dent corn inbred and a popcorn inbred, were evaluated for 4 PH traits. Quantitative trait loci (QTLs) for each trait were detected using composite interval mapping methods. In addition, genetic interrelationships were investigated using multiple-trait joint analysis for PH with ear height (EH), and for PH with top height (TH). In total, 6, 5, 2, and 6 QTLs were identified for PH, EH, TH, and TH/PH in single-trait analysis, respectively. Joint-analysis data suggest a strong and complex genetic relationship between PH and EH, and between PH and EH, with no QTLs controlling any single trait independently. In addition, 4 kinds of QTLs detected were classified as closely linked QTLs, pleiotropic QTLs, QTLs with opposite effects, and additional QTLs. It was, consequently, difficult to improve lodge resistance through selection on any individual PH trait. The current study demonstrates that multiple-trait joint analysis not only identified additional QTLs, but also revealed the genetic relationship among different highly correlated traits at the molecular level.

Dissecting the complex genetic basis of mate choice

The genetic analysis of mate choice is fraught with difficulties. Males produce complex signals and displays that can consist of a combination of acoustic, visual, chemical and behavioural phenotypes. Furthermore, female preferences for these male traits are notoriously difficult to quantify. During mate choice, genes not only affect the phenotypes of the individual they are in, but can influence the expression of traits in other individuals. How can genetic analyses be conducted to encompass this complexity? Tighter integration of classical quantitative genetic approaches with modern genomic technologies promises to advance our understanding of the complex genetic basis of mate choice.

Bayesian reanalysis of a quantitative trait locus accounting for multiple environments by scaling in broilers1

A Bayesian method was developed to handle QTL analyses of multiple experimental data of outbred populations with heterogeneity of variance between sexes for all random effects. The method employed a scaled reduced animal model with random polygenic and QTL allelic effects. A parsimonious model specification was applied by choosing assumptions regarding the covariance structure to limit the number of parameters to estimate. Markov chain Monte Carlo algorithms were applied to obtain marginal posterior densities. Simulation demonstrated that joint analysis of multiple environments is more powerful than separate single trait analyses of each environment. Measurements on broiler BW obtained from 2 experiments concerning growth efficiency and carcass traits were used to illustrate the method. The population consisted of 10 full-sib families from a cross between 2 broiler lines. Microsatellite genotypes were determined on generations 1 and 2, and phenotypes were collected on groups of generation 3 animals. The model included a polygenic correlation, which had a posterior mean of 0.70 in the analyses. The reanalysis agreed on the presence of a QTL in marker bracket MCW0058-LEI0071 accounting for 34% of the genetic variation in males and 24% in females in the growth efficiency experiment. In the carcass experiment, this QTL accounted for 19% of the genetic variation in males and 6% in females.