Multiple trait analysis of genetic mapping for quantitative trait loci

Genetic variants in LPL, OASL and TOMM40/APOE-C1-C2-C4 genes are associated with multiple cardiovascular-related traits

BACKGROUND

Genome-wide association studies (GWAS) have become a major strategy for genetic dissection of human complex diseases. Analysing multiple phenotypes jointly may improve both our ability to detect genetic variants with multiple effects and our understanding of their common features. Allelic associations for multiple biochemical traits (serum alanine aminotransferase, aspartate aminotransferase, butrylycholinesterase (BCHE), C-reactive protein (CRP), ferritin, gamma glutamyltransferase (GGT), glucose, high-density lipoprotein cholesterol (HDL), insulin, low-density lipoprotein cholesterol (LDL), triglycerides and uric acid), and body-mass index, were examined.

METHODS

We aimed to identify common genetic variants affecting more than one of these traits using genome-wide association analysis in 2548 adolescents and 9145 adults from 4986 Australian twin families. Multivariate and univariate associations were performed.

RESULTS

Multivariate analyses identified eight loci, and univariate association analyses confirmed two loci influencing more than one trait at p < 5 × 10-8. These are located on chromosome 8 (LPL gene affecting HDL and triglycerides) and chromosome 19 (TOMM40/APOE-C1-C2-C4 gene cluster affecting LDL and CRP). A locus on chromosome 12 (OASL gene) showed effects on GGT, LDL and CRP. The loci on chromosomes 12 and 19 unexpectedly affected LDL cholesterol and CRP in opposite directions.

CONCLUSIONS

We identified three possible loci that may affect multiple traits and validated 17 previously-reported loci. Our study demonstrated the usefulness of examining multiple phenotypes jointly and highlights an anomalous effect on CRP, which is increasingly recognised as a marker of cardiovascular risk as well as of inflammation.

Functional mapping of dynamic traits with robust t-distribution

Functional mapping has been a powerful tool in mapping quantitative trait loci (QTL) underlying dynamic traits of agricultural or biomedical interest. In functional mapping, multivariate normality is often assumed for the underlying data distribution, partially due to the ease of parameter estimation. The normality assumption however could be easily violated in real applications due to various reasons such as heavy tails or extreme observations. Departure from normality has negative effect on testing power and inference for QTL identification. In this work, we relax the normality assumption and propose a robust multivariate -distribution mapping framework for QTL identification in functional mapping. Simulation studies show increased mapping power and precision with the distribution than that of a normal distribution. The utility of the method is demonstrated through a real data analysis.

A complete solution for dissecting pure main and epistatic effects of QTL in triple testcross design

Epistasis plays an important role in genetics, evolution and crop breeding. To detect the epistasis, triple test cross (TTC) design had been developed several decades ago. Classical procedures for the TTC design use only linear transformations Z(1), Z(2) and Z(3), calculated from the TTC family means of quantitative trait, to infer the nature of the collective additive, dominance and epistatic effects of all the genes. Although several quantitative trait loci (QTL) mapping approaches in the TTC design have been developed, these approaches do not provide a complete solution for dissecting pure main and epistatic effects. In this study, therefore, we developed a two-step approach to estimate all pure main and epistatic effects in the F(2)-based TTC design under the F(2) and F(∞) metric models. In the first step, with Z(1) and Z(2) the augmented main and epistatic effects in the full genetic model that simultaneously considered all putative QTL on the whole genome were estimated using empirical Bayes approach, and with Z(3) three pure epistatic effects were obtained using two-dimensional genome scans. In the second step, the three pure epistatic effects obtained in the first step were integrated with the augmented epistatic and main effects for the further estimation of all other pure effects. A series of Monte Carlo simulation experiments has been carried out to confirm the proposed method. The results from simulation experiments show that: 1) the newly defined genetic parameters could be rightly identified with satisfactory statistical power and precision; 2) the F(2)-based TTC design was superior to the F(2) and F(2:3) designs; 3) with Z(1) and Z(2) the statistical powers for the detection of augmented epistatic effects were substantively affected by the signs of pure epistatic effects; and 4) with Z(3) the estimation of pure epistatic effects required large sample size and family replication number. The extension of the proposed method in this study to other base populations was further discussed.

Genetic architecture of life history traits and environment-specific trade-offs

Life history theory predicts the evolution of trait combinations that enhance fitness, and the occurrence of trade-offs depends in part on the magnitude of variation in growth rate or acquisition. Using recombinant inbred lines, we examined the genetic architecture of age and size at reproduction across abiotic conditions encountered by cultivars and naturalized populations of Brassica rapa. We found that genotypes are plastic to seasonal setting, such that reproduction was accelerated under conditions encountered by summer annual populations and genetic variances for age at reproduction varied across simulated seasonal settings. Using an acquisition-allocation model, we predicted the likelihood of trade-offs. Consistent with predicted relationships, we observed a trade-off where early maturity is associated with small size at maturity under simulated summer and fall annual conditions but not under winter annual conditions. The trade-off in the summer annual setting was observed despite significant genotypic variation in growth rate, which is often expected to decouple age and size at reproduction because rapidly growing genotypes could mature early and attain a larger size relative to slowly growing genotypes that mature later. The absence of a trade-off in the winter setting is presumably attributable to the absence of genotypic differences in age at reproduction. We observed QTL for age at reproduction that jointly regulated size at reproduction in both the summer and fall annual settings, but these QTL were environment-specific (i.e. different QTL contributed to the trade-off in the fall vs. summer annual settings). Thus, at least some of the genetic mechanisms underlying observed trade-offs differed across environments.

Network modeling of the transcriptional effects of copy number aberrations in glioblastoma

DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long- and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA- and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.

The genetic architecture of ecophysiological and circadian traits in Brassica rapa

Developmental mechanisms that enable perception of and response to the environment may enhance fitness. Ecophysiological traits typically vary depending on local conditions and contribute to resource acquisition and allocation, yet correlations may limit adaptive trait expression. Notably, photosynthesis and stomatal conductance vary diurnally, and the circadian clock, which is an internal estimate of time that anticipates diurnal light/dark cycles, may synchronize physiological behaviors with environmental conditions. Using recombinant inbred lines of Brassica rapa, we examined the quantitative-genetic architecture of ecophysiological and phenological traits and tested their association with the circadian clock. We also investigated how trait expression differed across treatments that simulated seasonal settings encountered by crops and naturalized populations. Many ecophysiological traits were correlated, and some correlations were consistent with expected biophysical constraints; for example, stomata jointly regulate photosynthesis and transpiration by affecting carbon dioxide and water vapor diffusion across leaf surfaces, and these traits were correlated. Interestingly, some genotypes had unusual combinations of ecophysiological traits, such as high photosynthesis in combination with low stomatal conductance or leaf nitrogen, and selection on these genotypes could provide a mechanism for crop improvement. At the genotypic and QTL level, circadian period was correlated with leaf nitrogen, instantaneous measures of photosynthesis, and stomatal conductance as well as with a long-term proxy (carbon isotope discrimination) for gas exchange, suggesting that gas exchange is partly regulated by the clock and thus synchronized with daily light cycles. The association between circadian rhythms and ecophysiological traits is relevant to crop improvement and adaptive evolution.

Mining for genotype-phenotype relations in Saccharomyces using partial least squares

Background

Multivariate approaches are important due to their versatility and applications in many fields as it provides decisive advantages over univariate analysis in many ways. Genome wide association studies are rapidly emerging, but approaches in hand pay less attention to multivariate relation between genotype and phenotype. We introduce a methodology based on a BLAST approach for extracting information from genomic sequences and Soft- Thresholding Partial Least Squares (ST-PLS) for mapping genotype-phenotype relations.

Results

Applying this methodology to an extensive data set for the model yeast Saccharomyces cerevisiae, we found that the relationship between genotype-phenotype involves surprisingly few genes in the sense that an overwhelmingly large fraction of the phenotypic variation can be explained by variation in less than 1% of the full gene reference set containing 5791 genes. These phenotype influencing genes were evolving 20% faster than non-influential genes and were unevenly distributed over cellular functions, with strong enrichments in functions such as cellular respiration and transposition. These genes were also enriched with known paralogs, stop codon variations and copy number variations, suggesting that such molecular adjustments have had a disproportionate influence on Saccharomyces yeasts recent adaptation to environmental changes in its ecological niche.

Conclusions

BLAST and PLS based multivariate approach derived results that adhere to the known yeast phylogeny and gene ontology and thus verify that the methodology extracts a set of fast evolving genes that capture the phylogeny of the yeast strains. The approach is worth pursuing, and future investigations should be made to improve the computations of genotype signals as well as variable selection procedure within the PLS framework.

Simultaneous estimation of multiple quantitative trait loci and growth curve parameters through hierarchical Bayesian modeling

A novel hierarchical quantitative trait locus (QTL) mapping method using a polynomial growth function and a multiple-QTL model (with no dependence in time) in a multitrait framework is presented. The method considers a population-based sample where individuals have been phenotyped (over time) with respect to some dynamic trait and genotyped at a given set of loci. A specific feature of the proposed approach is that, instead of an average functional curve, each individual has its own functional curve. Moreover, each QTL can modify the dynamic characteristics of the trait value of an individual through its influence on one or more growth curve parameters. Apparent advantages of the approach include: (1) assumption of time-independent QTL and environmental effects, (2) alleviating the necessity for an autoregressive covariance structure for residuals and (3) the flexibility to use variable selection methods. As a by-product of the method, heritabilities and genetic correlations can also be estimated for individual growth curve parameters, which are considered as latent traits. For selecting trait-associated loci in the model, we use a modified version of the well-known Bayesian adaptive shrinkage technique. We illustrate our approach by analysing a sub sample of 500 individuals from the simulated QTLMAS 2009 data set, as well as simulation replicates and a real Scots pine (Pinus sylvestris) data set, using temporal measurements of height as dynamic trait of interest.

Quantitative trait loci for longevity in heat-stressed Drosophila melanogaster

Longevity is a typical quantitative trait which is influenced by multiple genes. Here we explore the genetic variation in longevity of Drosophila melanogaster in both mildly heat-stressed and control flies. Quantitative trait loci (QTL) analysis for longevity was performed in a single-sex environment at 25°C with and without a mild heat-stress pre-treatment, using a previously reported set of recombinant inbred lines (RIL). QTL regions for longevity in heat-stressed flies overlapped with QTL for longevity in control flies. All longevity QTL co-localized with QTL for longevity identified in previous studies using very different sets of RIL in mixed sex environments, though the genome is nearly saturated with QTL for longevity when considering all previous studies. Heat stress decreased the number of significant QTL for longevity if compared to the control environment. Our mild heat-stress pre-treatment had a beneficial effect (hormesis) more often in shorter-lived than in longer-lived RIL.

Mapping QTL for grain yield, yield components, and spike features in a doubled haploid population of bread wheat

A doubled haploid (DH) population derived from a cross between the Japanese cultivar 'Fukuho-kumogi' and the Israeli wheat line 'Oligoculm' was used to map genome regions involved in the expression of grain yield, yield components, and spike features in wheat (Triticum aestivum L). A total of 371 markers (RAPD, SSR, RFLP, AFLP, and two morphological traits) were used to construct the linkage map that covered 4190 cM of wheat genome including 28 linkage groups. The results of composite interval mapping for all studied traits showed that some of the quantitative trait loci (QTL) were stable over experiments conducted in 2004 and 2005. The major QTL located in the Hair-Xpsp2999 interval on chromosome 1A controlled the expression of grains/spike (R(2) = 12.9% in 2004 and 22.4% in 2005), grain weight/spike (R(2) = 21.4% in 2004 and 15.8% in 2005), and spike number (R(2) = 15.6% in 2004 and 5.4% in 2005). The QTL for grain yield located on chromosomes 6A, 6B, and 6D totally accounted for 27.2% and 31.7% of total variation in this trait in 2004 and 2005, respectively. Alleles inherited from 'Oligoculm' increased the length of spikes and had decreasing effects on spike number. According to the data obtained in 2005, locus Xgwm261 was associated with a highly significant spike length QTL (R(2) = 42.33%) and also the major QTL for spikelet compactness (R(2) = 26.1%).

Multigenic control and sex bias in host susceptibility to spore-induced pulmonary anthrax in mice

Mechanisms underlying susceptibility to anthrax infection are unknown. Using a phylogenetically diverse panel of inbred mice and spores of Bacillus anthracis Ames, we investigated host susceptibility to pulmonary anthrax. Susceptibility profiles for survival time and organ pathogen load differed across strains, indicating distinct genetic controls. Tissue infection kinetics analysis showed greater systemic dissemination in susceptible DBA/2J (D) mice but a higher terminal bacterial load in resistant BALB/cJ (C) mice. Interestingly, the most resistant strains, C and C57BL/6J (B), demonstrated a sex bias for susceptibility. For example, BALB/cJ females had a significantly higher survival time and required 4-fold more spores for 100% mortality compared to BALB/cJ males. To identify genetic regions associated with differential susceptibility, survival time and extent of organ infection were assessed using mice derived from two susceptibility models: (i) BXD advanced recombinant inbred strains and (ii) F2 offspring generated from polar responding C and D strains. Genome-wide analysis of BXD strain survival identified linkage on chromosomes 5, 6, 9, 11, and 14. Quantitative trait locus (QTL) analysis of the C×DF2 population revealed a significant QTL (designated Rpai1 for resistance to pulmonary anthrax infection, locus 1) for survival time on chromosome 17 and also identified a chromosome 11 locus for lung pathogen burden. The striking difference between genome-wide linkage profiles for these two mouse models of anthrax susceptibility supports our hypothesis that these are multigenic traits. Our data provide the first evidence for a differential sex response to anthrax resistance and further highlight the unlikelihood of a single common genetic contribution for this response across strains.

Bivariate association analysis in selected samples: application to a GWAS of two bone mineral density phenotypes in males with high or low BMD

Our specific aims were to evaluate the power of bivariate analysis and to compare its performance with traditional univariate analysis in samples of unrelated subjects under varying sampling selection designs. Bivariate association analysis was based on the seemingly unrelated regression (SUR) model that allows different genetic models for different traits. We conducted extensive simulations for the case of two correlated quantitative phenotypes, with the quantitative trait locus making equal or unequal contributions to each phenotype. Our simulation results confirmed that the power of bivariate analysis is affected by the size, direction and source of the phenotypic correlations between traits. They also showed that the optimal sampling scheme depends on the size and direction of the induced genetic correlation. In addition, we demonstrated the efficacy of SUR-based bivariate test by applying it to a real Genome-Wide Association Study (GWAS) of Bone Mineral Density (BMD) values measured at the lumbar spine (LS) and at the femoral neck (FN) in a sample of unrelated males with low BMD (LS Z-scores ≤ -2) and with high BMD (LS and FN Z-scores >0.5). A substantial amount of top hits in bivariate analysis did not reach nominal significance in any of the two single-trait analyses. Altogether, our studies suggest that bivariate analysis is of practical significance for GWAS of correlated phenotypes.

QTL mapping of 1000-kernel weight, kernel length, and kernel width in bread wheat (Triticum aestivum L.)

Kernel size and morphology influence the market value and milling yield of bread wheat (Triticum aestivum L.). The objective of this study was to identify quantitative trait loci (QTLs) controlling kernel traits in hexaploid wheat. We recorded 1000-kernel weight, kernel length, and kernel width for 185 recombinant inbred lines from the cross Rye Selection 111 &times; Chinese Spring grown in 2 agro-climatic regions in India for many years. Composite interval mapping (CIM) was employed for QTL detection using a linkage map with 169 simple sequence repeat (SSR) markers. For 1000-kernel weight, 10 QTLs were identified on wheat chromosomes 1A, 1D, 2B, 2D, 4B, 5B, and 6B, whereas 6 QTLs for kernel length were detected on 1A, 2B, 2D, 5A, 5B and 5D. Chromosomes 1D, 2B, 2D, 4B, 5B and 5D had 9 QTLs for kernel width. Chromosomal regions with QTLs detected consistently for multiple year-location combinations were identified for each trait. Pleiotropic QTLs were found on chromosomes 2B, 2D, 4B, and 5B. The identified genomic regions controlling wheat kernel size and shape can be targeted during further studies for their genetic dissection.

Linkage mapping of principal components for femoral biomechanical performance in a reciprocal HCB-8 × HCB-23 intercross

Studies of bone genetics have addressed an array of related phenotypes, including various measures of biomechanical performance, bone size, bone, shape, and bone mineral density. These phenotypes are not independent, resulting in redundancy of the information they provide. Principal component (PC) analysis transforms multiple phenotype data to a new set of orthogonal "synthetic" phenotypes. We performed PC analysis on 17 femoral biomechanical, anatomic, and body size phenotypes in a reciprocal intercross of HcB-8 and HcB-23, accounting for 80% of the variance in 4 PCs. Three of the 4 PCs were mapped in the cross. The linkage analysis revealed a quantitative trait locus (QTL) with LOD = 4.7 for PC2 at 16 cM on chromosome 19 that was not detected using the directly measured phenotypes. The chromosome 19 QTL falls within a ~10 megabase interval, with Osf1 as a positional candidate gene. PC QTLs were also found on chromosomes 1, 2, 4, 6, and 10 that coincided with those identified for directly measured or calculated material property phenotypes. The novel chromosome 19 QTL illustrates the power advantage that attends use of PC phenotypes for linkage mapping. Constraint of the chromosome 19 candidate interval illustrates an important advantage of experimental crosses between recombinant congenic mouse strains.

Structural equation modeling of gene-environment interactions in coronary heart disease

Coronary heart disease (CHD) is a complex disease, which is influenced not only by genetic and environmental factors but also by gene-environment (GE) interactions in interconnected biological pathways or networks. The classical methods are inadequate for identifying GE interactions due to the complex relationships among risk factors, mediating risk factors (e.g., hypertension, blood lipids, and glucose), and CHD. Our aim was to develop a two-level structural equation model (SEM) to identify genes and GE interactions in the progress of CHD to take into account the causal structure among mediating risk factors and CHD (Level 1), and hierarchical family structure (Level 2). The method was applied to the Framingham Heart Study (FHS) Offspring Cohort data. Our approach has several advantages over classical methods: (1) it provides important insight into how genes and contributing factors affect CHD by investigating the direct, indirect, and total effects; and (2) it aids the development of biological models that more realistically reflect the complex biological pathways or networks. Using our method, we are able to detect GE interaction of SERPINE1 and body mass index (BMI) on CHD, which has not been reported. We conclude that SEM modeling of GE interaction can be applied in the analysis of complex epidemiological data sets.

A genetical genomics approach to genome scans increases power for QTL mapping

We describe a method for integrating gene expression information into genome scans and show that this can substantially increase the statistical power of QTL mapping. The method has three stages. First, standard clustering methods identify small (size 5-20) groups of genes with similar expression patterns. Second, each gene group is tested for a causative genetic locus shared with the clinical trait of interest. This is done using an EM algorithm approach that treats genotype at the putative causative locus as an unobserved variable and combines expression information from all of the genes in the group to infer genotype information at the locus. Finally, expression QTL (eQTL) are mapped for each gene group that shares a causative locus with the clinical trait. Such eQTL are candidates for the causative locus. Simulation results show that this method has far superior power to standard QTL mapping techniques in many circumstances. We applied this method to existing data on mouse obesity. Our method identified 27 putative body weight QTL, whereas standard QTL mapping produced only one. Furthermore, most gene groups with body weight QTL included cis genes, so candidate genes could be immediately identified. Eleven body weight QTL produced 16 candidate genes that have been previously associated with body weight or body weight-related traits, thus validating our method. In addition, 15 of the 16 other loci produced 32 candidate genes that have not been associated with body weight. Thus, this method shows great promise for finding new causative loci for complex traits.

Bayesian models for detecting epistatic interactions from genetic data

Current disease association studies are routinely conducted on a genome-wide scale, testing hundreds of thousands or millions of genetic markers. Besides detecting marginal associations of individual markers with the disease, it is also of interest to identify gene-gene and gene-environment interactions, which confer susceptibility to the disease risk. The astronomical number of possible combinations of markers and environmental factors, however, makes interaction mapping a daunting task both computationally and statistically. In this paper, we review and discuss a set of Bayesian partition methods developed recently for mapping single-nucleotide polymorphisms in case-control studies, their extension to quantitative traits, and further generalization to multiple traits. We use simulation and real data sets to demonstrate the performance of these methods, and we compare them with some existing interaction mapping algorithms. With the recent advance in high-throughput sequencing technologies, genome-wide measurements of epigenetic factor enrichment, structural variations, and transcription activities become available at the individual level. The tsunami of data creates more challenges for gene-gene interaction mapping, but at the same time provides new opportunities that, if utilized properly through sophisticated statistical means, can improve the power of mapping interactions at the genome scale.

Bayesian mixture structural equation modelling in multiple-trait QTL mapping

Quantitative trait loci (QTLs) mapping often results in data on a number of traits that have well-established causal relationships. Many multi-trait QTL mapping methods that account for correlation among the multiple traits have been developed to improve the statistical power and the precision of QTL parameter estimation. However, none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the QTL may not be fully understood. In this paper, we developed a Bayesian multiple QTL mapping method for causally related traits using a mixture structural equation model (SEM), which allows researchers to decompose QTL effects into direct, indirect and total effects. Parameters are estimated based on their marginal posterior distribution. The posterior distributions of parameters are estimated using Markov Chain Monte Carlo methods such as the Gibbs sampler and the Metropolis-Hasting algorithm. The number of QTLs affecting traits is determined by the Bayes factor. The performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait Bayesian analysis, our proposed method not only improved the statistical power of QTL detection, accuracy and precision of parameter estimates but also provided important insight into how genes regulate traits directly and indirectly by fitting a more biologically sensible model.

Principal-component-based multivariate regression for genetic association studies of metabolic syndrome components

Background

Quantitative traits often underlie risk for complex diseases. For example, weight and body mass index (BMI) underlie the human abdominal obesity-metabolic syndrome. Many attempts have been made to identify quantitative trait loci (QTL) over the past decade, including association studies. However, a single QTL is often capable of affecting multiple traits, a quality known as gene pleiotropy. Gene pleiotropy may therefore cause a loss of power in association studies focused only on a single trait, whether based on single or multiple markers.

Results

We propose using principal-component-based multivariate regression (PCBMR) to test for gene pleiotropy with comprehensive evaluation. This method generates one or more independent canonical variables based on the principal components of original traits and conducts a multivariate regression to test for association with these new variables. Systematic simulation studies have shown that PCBMR has great power. PCBMR-based pleiotropic association studies of abdominal obesity-metabolic syndrome and its possible linkage to chromosomal band 3q27 identified 11 susceptibility genes with significant associations. Whereas some of these genes had been previously reported to be associated with metabolic traits, others had never been identified as metabolism-associated genes.

Conclusions

PCBMR is a computationally efficient and powerful test for gene pleiotropy. Application of PCBMR to abdominal obesity-metabolic syndrome indicated the existence of gene pleiotropy affecting this syndrome.

Multi-environment QTL mapping in blackcurrant (Ribes nigrum L.) using mixed models

The first genetic linkage map of blackcurrant, published by Brennan et al. (Euphytica 161:19-34, 2008), identified regions where quantitative trait loci (QTLs) for some important traits were located. The analysis was complicated by the fact that the mapping population was found to contain two subgroups, with segregation ratios consistent with these being crossed and selfed offspring. The QTL analysis was based on the trait mean over 3 years and focused on the crossed offspring. Here we proposed a mixed model multi-environment approach for this population. The 3 years are considered as three separate environments, the data from both the selfed and crossed offspring are combined and different residual terms are explored to model the correlation between the years. This permits tests for interactions between the QTLs, the year and the type of offspring (selfed or crossed). This is applied to re-analyse two important traits, anthocyanin concentration and budbreak. Several additional QTLs were identified, some affecting the trait in both the selfed and crossed offspring, others in just one.

Human behavioral informatics in genetic studies of neuropsychiatric disease: multivariate profile-based analysis

While genome-wide association (GWA) studies have yielded notable findings with regard to the identification of risk variants in diseases such as obesity and diabetes, similar studies of schizophrenia - and neuropsychiatric diseases in general - have failed to produce strong findings. One, plausible explanation for this relates to phenotypic heterogeneity and what may be inherent imprecision associated with diagnostic categories in neuropsychiatric disorders. In this review we discuss a general approach to addressing the problem of heterogeneity that draws on concepts in behavioral informatics and the use of multivariable behavioral profiles in genetic studies of neuropsychiatric disease. The use of behavioral profiles as phenotypes eliminates the need for categorizing individuals with different 'subtypes' of a disease into one group and provides a way to investigate genetic susceptibility to different neuropsychiatric disorders that share similar clinical characteristics, such as schizophrenia and bipolar disorder. Further, behavioral profiles are a direct, quantitative representation of the emotional, personality, and neurocognitive functioning of the individuals being studied, and as such, the use of these profiles may provide increased statistical power to detect genetic associations and linkages. We describe and discuss four general data analysis approaches that can be used to analyze and integrate multivariate behavioral profile data and high-dimensional genomic data. Ultimately, we propose that behavioral profile-based phenotypes provide a meaningful alternative to the use of single measures, such as diagnostic category, in genetic association studies of neuropsychiatric disease.

Data-driven assessment of eQTL mapping methods

Background

The analysis of expression quantitative trait loci (eQTL) is a potentially powerful way to detect transcriptional regulatory relationships at the genomic scale. However, eQTL data sets often go underexploited because legacy QTL methods are used to map the relationship between the expression trait and genotype. Often these methods are inappropriate for complex traits such as gene expression, particularly in the case of epistasis.

Results

Here we compare legacy QTL mapping methods with several modern multi-locus methods and evaluate their ability to produce eQTL that agree with independent external data in a systematic way. We found that the modern multi-locus methods (Random Forests, sparse partial least squares, lasso, and elastic net) clearly outperformed the legacy QTL methods (Haley-Knott regression and composite interval mapping) in terms of biological relevance of the mapped eQTL. In particular, we found that our new approach, based on Random Forests, showed superior performance among the multi-locus methods.

Conclusions

Benchmarks based on the recapitulation of experimental findings provide valuable insight when selecting the appropriate eQTL mapping method. Our battery of tests suggests that Random Forests map eQTL that are more likely to be validated by independent data, when compared to competing multi-locus and legacy eQTL mapping methods.

Quantitative trait loci mapping of leaf angle and leaf orientation value in maize (Zea mays L.)

A major limiting factor for high productivity of maize (Zea mays L.) in dense planting is light penetration through the canopy. Plant architecture with a narrower leaf angle (LA) and an optimum leaf orientation value (LOV) is desirable to increase light capture for photosynthesis and production per unit area. However, the genetic control of the plant architecture traits remains poorly understood in maize. In this study, QTL for LA, LOV, and related traits were mapped using a set of 229 F(2:3) families derived from the cross between compact and expanded inbred lines, evaluated in three environments. Twenty-five QTL were detected in total. Three of the QTL explained 37.4% and five of the QTL explained 53.9% of the phenotypic variance for LA and LOV, respectively. Two key genome regions controlling leaf angle and leaf orientation were identified. qLA1 and qLOV1 at nearest marker umc2226 on chromosome 1.02 accounted for 20.4 and 23.2% of the phenotypic variance, respectively; qLA5 and qLOV5 at nearest bnlg1287 on chromosome 5 accounted for 9.7 and 9.8% of the phenotypic variance, respectively. These QTL could provide useful information for marker-assisted selection in improving performance of plant architecture with regard to leaf angle and orientation.

Mapping environment-specific quantitative trait loci

Environment-specific quantitative trait loci (QTL) refer to QTL that express differently in different environments, a phenomenon called QTL-by-environment (Q × E) interaction. Q × E interaction is a difficult problem extended from traditional QTL mapping. The mixture model maximum-likelihood method is commonly adopted for interval mapping of QTL, but the method is not optimal in handling QTL interacting with environments. We partitioned QTL effects into main and interaction effects. The main effects are represented by the means of QTL effects in all environments and the interaction effects are represented by the variances of the QTL effects across environments. We used the Markov chain Monte Carlo (MCMC) implemented Bayesian method to estimate both the main and the interaction effects. The residual error covariance matrix was modeled using the factor analytic covariance structure. A simulation study showed that the factor analytic structure is robust and can handle other structures as special cases. The method was also applied to Q × E interaction mapping for the yield trait of barley. Eight markers showed significant main effects and 18 markers showed significant Q × E interaction. The 18 interacting markers were distributed across all seven chromosomes of the entire genome. Only 1 marker had both the main and the Q × E interaction effects. Each of the other markers had either a main effect or a Q × E interaction effect but not both.

Genetic and genomic tools to improve drought tolerance in wheat

Tolerance to drought is a quantitative trait, with a complex phenotype, often confounded by plant phenology. Breeding for drought tolerance is further complicated since several types of abiotic stress, such as high temperatures, high irradiance, and nutrient toxicities or deficiencies can challenge crop plants simultaneously. Although marker-assisted selection is now widely deployed in wheat, it has not contributed significantly to cultivar improvement for adaptation to low-yielding environments and breeding has relied largely on direct phenotypic selection for improved performance in these difficult environments. The limited success of the physiological and molecular breeding approaches now suggests that a careful rethink is needed of our strategies in order to understand better and breed for drought tolerance. A research programme for increasing drought tolerance of wheat should tackle the problem in a multi-disciplinary approach, considering interaction between multiple stresses and plant phenology, and integrating the physiological dissection of drought-tolerance traits and the genetic and genomics tools, such as quantitative trait loci (QTL), microarrays, and transgenic crops. In this paper, recent advances in the genetics and genomics of drought tolerance in wheat and barley are reviewed and used as a base for revisiting approaches to analyse drought tolerance in wheat. A strategy is then described where a specific environment is targeted and appropriate germplasm adapted to the chosen environment is selected, based on extensive definition of the morpho-physiological and molecular mechanisms of tolerance of the parents. This information was used to create structured populations and develop models for QTL analysis and positional cloning.

Is local adaptation in Mimulus guttatus caused by trade-offs at individual loci?

Local adaptation is considered to be the result of fitness trade-offs for particular phenotypes across different habitats. However, it is unclear whether such phenotypic trade-offs exist at the level of individual genetic loci. Local adaptation could arise from trade-offs of alternative alleles at individual loci or by complementary sets of loci with different fitness effects of alleles in one habitat but selective neutrality in the alternative habitat. To evaluate the genome-wide basis of local adaptation, we performed a field-based quantitative trait locus (QTL) mapping experiment on recombinant inbred lines (RILs) created from coastal perennial and inland annual races of the yellow monkeyflower (Mimulus guttatus) grown reciprocally in native parental habitats. Overall, we detected 19 QTLs affecting one or more of 16 traits measured in two environments, most of small effect. We identified 15 additional QTL effects at two previously identified candidate QTLs [DIVERGENCE (DIV)]. Significant QTL by environment interactions were detected at the DIV loci, which was largely attributable to genotypic differences at a single field site. We found no detectable evidence for trade-offs for any one component of fitness, although DIV2 showed a trade-off involving different fitness traits between sites, suggesting that local adaptation is largely controlled by non-overlapping loci. This is surprising for an outcrosser, implying that reduced gene flow prevents the evolution of individuals adapted to multiple environments. We also determined that native genotypes were not uniformly adaptive, possibly reflecting fixed mutational load in one of the populations.

Identification and validation of genes affecting aortic lesions in mice

Atherosclerosis represents the most significant risk factor for coronary artery disease (CAD), the leading cause of death in developed countries. To better understand the pathogenesis of atherosclerosis, we applied a likeli-hood-based model selection method to infer gene-disease causality relationships for the aortic lesion trait in a segregating mouse population demonstrating a spectrum of susceptibility to developing atherosclerotic lesions. We identified 292 genes that tested causal for aortic lesions from liver and adipose tissues of these mice, and we experimentally validated one of these candidate causal genes, complement component 3a receptor 1 (C3ar1), using a knockout mouse model. We also found that genes identified by this method overlapped with genes progressively regulated in the aortic arches of 2 mouse models of atherosclerosis during atherosclerotic lesion development. By comparing our gene set with findings from public human genome-wide association studies (GWAS) of CAD and related traits, we found that 5 genes identified by our study overlapped with published studies in humans in which they were identified as risk factors for multiple atherosclerosis-related pathologies, including myocardial infarction, serum uric acid levels, mean platelet volume, aortic root size, and heart failure. Candidate causal genes were also found to be enriched with CAD risk polymorphisms identified by the Wellcome Trust Case Control Consortium (WTCCC). Our findings therefore validate the ability of causality testing procedures to provide insights into the mechanisms underlying atherosclerosis development.

Nested association mapping for identification of functional markers

Identification of functional markers (FMs) provides information about the genetic architecture underlying complex traits. An approach that combines the strengths of linkage and association mapping, referred to as nested association mapping (NAM), has been proposed to identify FMs in many plant species. The ability to identify and resolve FMs for complex traits depends upon a number of factors including frequency of FM alleles, magnitudes of their genetic effects, disequilibrium among functional and nonfunctional markers, statistical analysis methods, and mating design. The statistical characteristics of power, accuracy, and precision to identify FMs with a NAM population were investigated using three simulation studies. The simulated data sets utilized publicly available genetic sequences and simulated FMs were identified using least-squares variable selection methods. Results indicate that FMs with simple additive genetic effects that contribute at least 5% to the phenotypic variability in at least five segregating families of a NAM population consisting of recombinant inbred progeny derived from 28 matings with a single reference inbred will have adequate power to accurately and precisely identify FMs. This resolution and power are possible even for genetic architectures consisting of disequilibrium among multiple functional and nonfunctional markers in the same genomic region, although the resolution of FMs will deteriorate rapidly if more than two FMs are tightly linked within the same amplicon. Finally, nested mating designs involving several reference parents will have a greater likelihood of resolving FMs than single reference designs.

Liver and adipose expression associated SNPs are enriched for association to type 2 diabetes

Genome-wide association studies (GWAS) have demonstrated the ability to identify the strongest causal common variants in complex human diseases. However, to date, the massive data generated from GWAS have not been maximally explored to identify true associations that fail to meet the stringent level of association required to achieve genome-wide significance. Genetics of gene expression (GGE) studies have shown promise towards identifying DNA variations associated with disease and providing a path to functionally characterize findings from GWAS. Here, we present the first empiric study to systematically characterize the set of single nucleotide polymorphisms associated with expression (eSNPs) in liver, subcutaneous fat, and omental fat tissues, demonstrating these eSNPs are significantly more enriched for SNPs that associate with type 2 diabetes (T2D) in three large-scale GWAS than a matched set of randomly selected SNPs. This enrichment for T2D association increases as we restrict to eSNPs that correspond to genes comprising gene networks constructed from adipose gene expression data isolated from a mouse population segregating a T2D phenotype. Finally, by restricting to eSNPs corresponding to genes comprising an adipose subnetwork strongly predicted as causal for T2D, we dramatically increased the enrichment for SNPs associated with T2D and were able to identify a functionally related set of diabetes susceptibility genes. We identified and validated malic enzyme 1 (Me1) as a key regulator of this T2D subnetwork in mouse and provided support for the association of this gene to T2D in humans. This integration of eSNPs and networks provides a novel approach to identify disease susceptibility networks rather than the single SNPs or genes traditionally identified through GWAS, thereby extracting additional value from the wealth of data currently being generated by GWAS.

Linkage mapping of femoral material properties in a reciprocal intercross of HcB-8 and HcB-23 recombinant mouse strains

Skeletal fragility is an important health problem with a large genetic component. We performed a 603 animal F2 reciprocal intercross of the recombinant congenic strains HcB-8 and HcB-23 to genetically map quantitative trait loci (QTLs) for tissue-level femoral biomechanical performance. These included elastic and post-yield strain, Young's modulus, elastic and maximum stress, and toughness and were calculated from 3-point bend testing of femora by the application of standard beam equations. We mapped these with R/qtl and QTL Cartographer and established significance levels empirically by permutation testing. Significant QTLs for at least one trait are present on chromosomes 1, 6, and 10 in the full F2 population, with additional QTLs evident in subpopulations defined by sex and cross direction. On chromosome 10, we find a QTL for post-yield strain and toughness, phenotypes that have not been mapped previously. Notably, the HcB-8 allele at this QTL increases post-yield strain and toughness, but decreases bone mineral density (BMD), while the material property QTLs on chromosomes 1, 6, and at a second chromosome 10 QTL are independent of BMD. We find significant sex x QTL and cross direction x QTL interactions. A robust, pleiotropic chromosome 4 QTL that we previously reported at the whole-bone level showed no evidence of linkage at the tissue-level, supporting our interpretation that modeling capacity is its primary phenotype. Our data demonstrate an inverse relationship between femoral perimeter and Young's modulus, with R(2)=0.27, supporting the view that geometric and material bone properties are subject to an integrated set of regulatory mechanisms. Mapping QTLs for tissue-level biomechanical performance advances understanding of the genetic basis of bone quality.

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.

Influence of day-length and isolates of Phytophthora infestans on field resistance to late blight of potato

Main and interaction effects of day-length and pathogen isolate on the reaction and expression of field resistance to Phytophthora infestans were analyzed in a sample of standard clones for partial resistance to potato late blight, and in the BCT mapping population derived from a backcross of Solanum berthaultii to Solanum tuberosum. Detached leaves from plants grown in field plots exposed to short- and long day-length conditions were independently inoculated with two P. infestans isolates and incubated in chambers under short- and long photoperiods, respectively. Lesion growth rate (LGR) was used for resistance assessment. Analysis of variance revealed a significant contribution of genotype x isolate x day-length interaction to variation in LGR indicating that field resistance of genotypes to foliar late blight under a given day-length depended on the infecting isolate. An allele segregating from S. berthaultii with opposite effects on foliar resistance to late blight under long- and short day-lengths, respectively, was identified at a quantitative trait locus (QTL) that mapped on chromosome 1. This allele was associated with positive (decreased resistance) and negative (increased resistance) additive effects on LGR, under short- and long day-length conditions, respectively. Disease progress on whole plants inoculated with the same isolate under field conditions validated the direction of its effect in short day-length regimes. The present study suggests the occurrence of an isolate-specific QTL that displays interaction with isolate behavior under contrasting environments, such as those with different day-lengths. This study highlights the importance of exposing genotypes to a highly variable population of the pathogen under contrasting environments when stability to late blight resistance is to be assessed or marker-assisted selection is attempted for the manipulation of quantitative resistance to late blight.

Bayesian analysis for genetic architecture of dynamic traits

The dissection of the genetic architecture of quantitative traits, including the number and locations of quantitative trait loci (QTL) and their main and epistatic effects, has been an important topic in current QTL mapping. We extend the Bayesian model selection framework for mapping multiple epistatic QTL affecting continuous traits to dynamic traits in experimental crosses. The extension inherits the efficiency of Bayesian model selection and the flexibility of the Legendre polynomial model fitting to the change in genetic and environmental effects with time. We illustrate the proposed method by simultaneously detecting the main and epistatic QTLs for the growth of leaf age in a doubled-haploid population of rice. The behavior and performance of the method are also shown by computer simulation experiments. The results show that our method can more quickly identify interacting QTLs for dynamic traits in the models with many numbers of genetic effects, enhancing our understanding of genetic architecture for dynamic traits. Our proposed method can be treated as a general form of mapping QTL for continuous quantitative traits, being easier to extend to multiple traits and to a single trait with repeat records.

Tomato Analyzer: a useful software application to collect accurate and detailed morphological and colorimetric data from two-dimensional objects

Measuring fruit morphology and color traits of vegetable and fruit crops in an objective and reproducible way is important for detailed phenotypic analyses of these traits. Tomato Analyzer (TA) is a software program that measures 37 attributes related to two-dimensional shape in a semi-automatic and reproducible manner^1,2. Many of these attributes, such as angles at the distal and proximal ends of the fruit and areas of indentation, are difficult to quantify manually. The attributes are organized in ten categories within the software: Basic Measurement, Fruit Shape Index, Blockiness, Homogeneity, Proximal Fruit End Shape, Distal Fruit End Shape, Asymmetry, Internal Eccentricity, Latitudinal Section and Morphometrics. The last category requires neither prior knowledge nor predetermined notions of the shape attributes, so morphometric analysis offers an unbiased option that may be better adapted to high-throughput analyses than attribute analysis. TA also offers the Color Test application that was designed to collect color measurements from scanned images and allow scanning devices to be calibrated using color standards³.

TA provides several options to export and analyze shape attribute, morphometric, and color data. The data may be exported to an excel file in batch mode (more than 100 images at one time) or exported as individual images. The user can choose between output that displays the average for each attribute for the objects in each image (including standard deviation), or an output that displays the attribute values for each object on the image. TA has been a valuable and effective tool for indentifying and confirming tomato fruit shape Quantitative Trait Loci (QTL), as well as performing in-depth analyses of the effect of key fruit shape genes on plant morphology. Also, TA can be used to objectively classify fruit into various shape categories. Lastly, fruit shape and color traits in other plant species as well as other plant organs such as leaves and seeds can be evaluated with TA.

Natural variation for seed dormancy in Arabidopsis is regulated by additive genetic and molecular pathways

Timing of germination is presumably under strong natural selection as it determines the environmental conditions in which a plant germinates and initiates its postembryonic life cycle. To investigate how seed dormancy is controlled, quantitative trait loci (QTL) analyses has been performed in six Arabidopsis thaliana recombinant inbred line populations by analyzing them simultaneously using a mixed model QTL approach. The recombinant inbred line populations were derived from crosses between the reference accession Landsberg erecta (Ler) and accessions from different world regions. In total, 11 delay of germination (DOG) QTL have been identified, and nine of them have been confirmed by near isogenic lines (NILs). The absence of strong epistatic interactions between the different DOG loci suggests that they affect dormancy mainly by distinct genetic pathways. This was confirmed by analyzing the transcriptome of freshly harvested dry seeds of five different DOG NILs. All five DOG NILs showed discernible and different expression patterns compared with the expression of their genetic background Ler. The genes identified in the different DOG NILs represent largely different gene ontology profiles. It is proposed that natural variation for seed dormancy in Arabidopsis is mainly controlled by different additive genetic and molecular pathways rather than epistatic interactions, indicating the involvement of several independent pathways.

Quantitative trait locus mapping of genes under selection across multiple years and sites in Avena barbata: epistasis, pleiotropy, and genotype-by-environment interactions

The genetic architecture of variation in evolutionary fitness determines the trajectory of adaptive change. We identified quantitative trait loci (QTL) affecting fitness in a mapping population of recombinant inbred lines (RILs) derived from a cross between moist- and dry- associated ecotypes of Avena barbata. We estimated fitness in 179 RILs in each of two natural environments in each of 4 years. Two loci account for over half of the variation in geometric mean fitness across environments. These loci are associated in repulsion phase in the wild ecotypes, suggesting the potential for strong transgressive segregation, but also show significant epistasis giving hybrid breakdown. This epistasis is the result of sharply lower fitness in only one of the recombinant genotypes, suggesting that the loci may contain synergistically acting mutations. Within each trial (year/site combination), we can explain less of the variation than for geometric mean fitness, but the two major loci are associated with variation in fitness in most environments. Tests for pleiotropic effects of QTL on fitness in different environments reveal that the same loci are under selection in all trials. Genotype-by-environment interactions are significant for some loci, but this reflects variation in the strength, not the direction of selection.

Mapping quantitative trait loci associated with chlorophyll a fluorescence parameters in soybean (Glycine max (L.) Merr.)

Chlorophyll a fluorescence parameters can provide qualitative and quantitative information about photosynthetic processes in chloroplasts. JIP-test and modulated fluorescence (MF) parameters are commonly used chlorophyll a fluorescence parameters. This study was conducted to identify quantitative trait loci (QTLs) associated with JIP-test parameters, MF parameters, and photosynthetic rate (P(N)), and to examine the relationships among them in soybean (Glycine max (L.) Merr.). Pot and field experiments were performed to evaluate 184 recombinant inbred lines (RILs) for five JIP-test parameters (ABS/RC, TR(O)/ABS, ET(O)/TR(O), RE(O)/ET(O), and PI(ABS)), four MF parameters (Fv/Fm, Fv'/Fm', PhiPSII, and qP), and P(N).Significant correlations were commonly observed among JIP-test parameters, MF parameters, and P(N). QTL mapping analysis identified 13, 9, and 4 QTLs for JIP-test parameters, MF parameters, and P(N), respectively, of which 13 were stable. Four major genomic regions were detected: LG A2 (19.81 cM) for JIP-test parameters, LG C1 (94.31 and 97.61 cM) for P(N) and MF parameters, LG M (100.51 cM) for JIP-test and MF parameters, and LG O (30.61-49.91 cM) for P(N), JIP-test, and MF parameters. These results indicate that chlorophyll fluorescence parameters, especially PHIPSII and qP, could play an important role in regulating P(N), and that JIP-test and MF parameters could be controlled by the same or different genes. The QTLs identified in this study will help in the understanding of the genetic basis of photosynthetic processes in plants. They will also contribute to the development of marker-assisted selection breeding programs for photosynthetic capacity in soybean.

The earliest stages of adaptation in an experimental plant population: strong selection on QTLS for seed dormancy

Colonizing species may often encounter strong selection during the initial stages of adaptation to novel environments. Such selection is particularly likely to act on traits expressed early in development since early survival is necessary for the expression of adaptive phenotypes later in life. Genetic studies of fitness under field conditions, however, seldom include the earliest developmental stages. Using a new set of recombinant inbred lines, we present a study of the genetic basis of fitness variation in Arabidopsis thaliana in which genotypes, environments, and geographic location were manipulated to study total lifetime fitness, beginning with the seed stage. Large-effect quantitative trait loci (QTLs) for fitness changed allele frequency and closely approached 90% in some treatments within a single generation. These QTLs colocated with QTLs for germination phenology when seeds were dispersed following a schedule of a typical winter annual, and they were detected in two geographic locations at different latitudes. Epistatically interacting loci affected both fitness and germination in many cases. QTLs for field germination phenology colocated with known QTLs for primary dormancy induction as assessed in laboratory tests, including the candidate genes DOG1 and DOG6. Therefore fitness, germination phenology, and primary dormancy are genetically associated at the level of specific chromosomal regions and candidate loci. Genes associated with the ability to arrest development at early life stages and assess environmental conditions are thereby likely targets of intense natural selection early in the colonization process.

QTLs for the elongation of axile and lateral roots of maize in response to low water potential

Changes in root architecture and the maintenance of root growth in drying soil are key traits for the adaptation of maize (Zea mays L.) to drought environments. The goal of this study was to map quantitative trait loci (QTLs) for root growth and its response to dehydration in a population of 208 recombinant inbred lines from the International Maize and Wheat Improvement Center (CIMMYT). The parents, Ac7643 and Ac7729/TZSRW, are known to be drought-tolerant and drought-sensitive, respectively. Roots were grown in pouches under well-watered conditions or at low water potential induced by the osmolyte polyethylene glycol (PEG 8000). Axile root length (L (Ax)) increased linearly, while lateral root length (L (Lat)) increased exponentially over time. Thirteen QTLs were identified for six seedling traits: elongation rates of axile roots (ER(Ax)), the rate constant of lateral root elongation (k (Lat)), the final respective lengths (L (Ax) and L (Lat)), and the ratios k (Lat)/ER(Ax) and L (Lat)/L (Ax.) While QTLs for lateral root traits were constitutively expressed, most QTLs for axile root traits responded to water stress. For axile roots, common QTLs existed for ER(Ax) and L (Ax). Quantitative trait loci for the elongation rates of axile roots responded more clearly to water stress compared to root length. Two major QTLs were detected: a QTL for general vigor in bin 2.02, affecting most of the traits, and a QTL for the constitutive increase in k (Lat) and k (Lat)/ER(Ax) in bins 6.04-6.05. The latter co-located with a major QTL for the anthesis-silking interval (ASI) reported in published field experiments, suggesting an involvement of root morphology in drought tolerance. Rapid seedling tests are feasible for elucidating the genetic response of root growth to low water potential. Some loci may even have pleiotropic effects on yield-related traits under drought stress.

A Bayesian partition method for detecting pleiotropic and epistatic eQTL modules

Studies of the relationship between DNA variation and gene expression variation, often referred to as “expression quantitative trait loci (eQTL) mapping”, have been conducted in many species and resulted in many significant findings. Because of the large number of genes and genetic markers in such analyses, it is extremely challenging to discover how a small number of eQTLs interact with each other to affect mRNA expression levels for a set of co-regulated genes. We present a Bayesian method to facilitate the task, in which co-expressed genes mapped to a common set of markers are treated as a module characterized by latent indicator variables. A Markov chain Monte Carlo algorithm is designed to search simultaneously for the module genes and their linked markers. We show by simulations that this method is more powerful for detecting true eQTLs and their target genes than traditional QTL mapping methods. We applied the procedure to a data set consisting of gene expression and genotypes for 112 segregants of S. cerevisiae. Our method identified modules containing genes mapped to previously reported eQTL hot spots, and dissected these large eQTL hot spots into several modules corresponding to possibly different biological functions or primary and secondary responses to regulatory perturbations. In addition, we identified nine modules associated with pairs of eQTLs, of which two have been previously reported. We demonstrated that one of the novel modules containing many daughter-cell expressed genes is regulated by AMN1 and BPH1. In conclusion, the Bayesian partition method which simultaneously considers all traits and all markers is more powerful for detecting both pleiotropic and epistatic effects based on both simulated and empirical data.

Genome-wide association studies (GWAS) have yielded several causal genes for many human diseases. However, the mechanisms underlying how DNA variations affect disease phenotypes have not been well understood in many cases. Gene expression is intermediate between DNA and clinical endpoints. Linking DNA variation and gene expression variation, often referred to as “expression quantitative trait loci (eQTL) mapping”, has yielded clues of mechanisms and pathways by which DNA variations impact phenotypes. Because of the large number of genes and genetic markers in such analyses, it is extremely challenging to discover how a small number of eQTLs interact with each other to affect mRNA expression levels for a set of co-regulated genes. We present a Bayesian method to identify genetic interactions and more eQTLs by treating co-expressed genes as a module. Our method provides a tool to study genetic interactions in human disease models.

High congruency of QTL positions for heterosis of grain yield in three crosses of maize

The genetic basis of heterosis in maize has been investigated in a number of studies but results have not been conclusive. Here, we compare quantitative trait loci (QTL) mapping results for grain yield, grain moisture, and plant height from three populations derived from crosses of the heterotic pattern Iowa Stiff Stalk Synthetic x Lancaster Sure Crop, investigated with the Design III, and analyzed with advanced statistical methods specifically developed to examine the genetic basis of mid-parent heterosis (MPH). In two populations, QTL analyses were conducted with a joint fit of linear transformations Z (1) (trait mean across pairs of backcross progenies) and Z (2) (half the trait difference between pairs of backcross progenies) to estimate augmented additive and augmented dominance effects of each QTL, as well as their ratio. QTL results for the third population were obtained from the literature. For Z (2) of grain yield, congruency of QTL positions was high across populations, and a large proportion of the genetic variance (~70%) was accounted for by QTL. This was not the case for Z (1) or the other two traits. Further, almost all congruent grain yield QTL were located in the same or an adjacent bin encompassing the centromere. We conclude that different alleles have been fixed in each heterotic pool, which in combination with allele(s) from the opposite heterotic pool lead to high MPH for grain yield. Their positive interactions very likely form the base line for the superior performance of the heterotic pattern under study.

Family-based bivariate association tests for quantitative traits

The availability of a large number of dense SNPs, high-throughput genotyping and computation methods promotes the application of family-based association tests. While most of the current family-based analyses focus only on individual traits, joint analyses of correlated traits can extract more information and potentially improve the statistical power. However, current TDT-based methods are low-powered. Here, we develop a method for tests of association for bivariate quantitative traits in families. In particular, we correct for population stratification by the use of an integration of principal component analysis and TDT. A score test statistic in the variance-components model is proposed. Extensive simulation studies indicate that the proposed method not only outperforms approaches limited to individual traits when pleiotropic effect is present, but also surpasses the power of two popular bivariate association tests termed FBAT-GEE and FBAT-PC, respectively, while correcting for population stratification. When applied to the GAW16 datasets, the proposed method successfully identifies at the genome-wide level the two SNPs that present pleiotropic effects to HDL and TG traits.

Association mapping of cadmium, copper and hydrogen peroxide tolerance of roots and translocation capacities of cadmium and copper in Arabidopsis thaliana

Association mapping analysis of Cd, Cu and H (2)O (2) tolerance, judged by relative root length (RRL: % of root length in stress condition relative to that in control condition), and Cd and Cu translocation ratios (amount of metal in the shoot to the total) were performed using 90 accessions of Arabidopsis thaliana. Using 140 SNPs that were distributed across the genome, association mapping analysis was performed with a haploid setting by the Q + K method, which minimizes detection of false associations by combining the Q-matrix of the structured association (Q) with kinship (K) to control for the population structure. Six, five and five significant (-log (10)P-value is 1.3 > or =) linkages were detected between the SNPs and Cd, Cu and H(2)O(2) resistant RRLs, respectively. In addition, six significant linkages were identified with translocation capacities of Cd and Cu. Among those detected loci, two each of Cu and Cd tolerance RRLs were collocated with those of H(2)O(2) tolerance RRL, while one locus each was detected by Cu and Cd tolerance RRLs that collocated with their translocation ratios. These results suggested that these factors might partly explain the phenotypic variation of tolerance RRLs to Cd and Cu of Arabidopsis thaliana. Finally, using a different approach to analyze interactions between individual phenotypes, namely clustering analysis, we found an expected segregation of resistant SNPs (single-nucleotide polymorphisms) of the multiple RRLs in the typical accession groups carrying multiple traits. Almost none of the loci detected by association mapping analysis were linked to the loci of previously identified critical genes regulating the traits, suggesting that this could be useful to identify complex architecture of genetic factors determining variation among multiple accessions.