Multiple trait analysis of genetic mapping for quantitative trait loci

Genotype×environment interaction QTL mapping in plants: lessons from Arabidopsis

Plant growth and development are influenced by the genetic composition of the plant (G), the environment (E), and the interaction between them (G×E). To produce suitable genotypes for multiple environments, G×E should be accounted for and assessed in plant-breeding programs. Here, we review the genetic basis of G×E and its consequence for quantitative trait loci (QTL) mapping in biparental and genome-wide association (GWA) mapping populations. We also consider the implications of G×E for understanding plant fitness trade-offs and evolutionary ecology.

Effect of advanced intercrossing on genome structure and on the power to detect linked quantitative trait loci in a multi-parent population: a simulation study in rice

Background

In genetic analysis of agronomic traits, quantitative trait loci (QTLs) that control the same phenotype are often closely linked. Furthermore, many QTLs are localized in specific genomic regions (QTL clusters) that include naturally occurring allelic variations in different genes. Therefore, linkage among QTLs may complicate the detection of each individual QTL. This problem can be resolved by using populations that include many potential recombination sites. Recently, multi-parent populations have been developed and used for QTL analysis. However, their efficiency for detection of linked QTLs has not received attention. By using information on rice, we simulated the construction of a multi-parent population followed by cycles of recurrent crossing and inbreeding, and we investigated the resulting genome structure and its usefulness for detecting linked QTLs as a function of the number of cycles of recurrent crossing.

Results

The number of non-recombinant genome segments increased linearly with an increasing number of cycles. The mean and median lengths of the non-recombinant genome segments decreased dramatically during the first five to six cycles, then decreased more slowly during subsequent cycles. Without recurrent crossing, we found that there is a risk of missing QTLs that are linked in a repulsion phase, and a risk of identifying linked QTLs in a coupling phase as a single QTL, even when the population was derived from eight parental lines. In our simulation results, using fewer than two cycles of recurrent crossing produced results that differed little from the results with zero cycles, whereas using more than six cycles dramatically improved the power under most of the conditions that we simulated.

Conclusion

Our results indicated that even with a population derived from eight parental lines, fewer than two cycles of crossing does not improve the power to detect linked QTLs. However, using six cycles dramatically improved the power, suggesting that advanced intercrossing can help to resolve the problems that result from linkage among QTLs.

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.

Mixed linear model approach for mapping quantitative trait loci underlying crop seed traits

The crop seed is a complex organ that may be composed of the diploid embryo, the triploid endosperm and the diploid maternal tissues. According to the genetic features of seed characters, two genetic models for mapping quantitative trait loci (QTLs) of crop seed traits are proposed, with inclusion of maternal effects, embryo or endosperm effects of QTL, environmental effects and QTL-by-environment (QE) interactions. The mapping population can be generated either from double back-cross of immortalized F2 (IF2) to the two parents, from random-cross of IF2 or from selfing of IF2 population. Candidate marker intervals potentially harboring QTLs are first selected through one-dimensional scanning across the whole genome. The selected candidate marker intervals are then included in the model as cofactors to control background genetic effects on the putative QTL(s). Finally, a QTL full model is constructed and model selection is conducted to eliminate false positive QTLs. The genetic main effects of QTLs, QE interaction effects and the corresponding P-values are computed by Markov chain Monte Carlo algorithm for Gaussian mixed linear model via Gibbs sampling. Monte Carlo simulations were performed to investigate the reliability and efficiency of the proposed method. The simulation results showed that the proposed method had higher power to accurately detect simulated QTLs and properly estimated effect of these QTLs. To demonstrate the usefulness, the proposed method was used to identify the QTLs underlying fiber percentage in an upland cotton IF2 population. A computer software, QTLNetwork-Seed, was developed for QTL analysis of seed traits.

Identification of QTLs associated with oil content in a high-oil Brassica napus cultivar and construction of a high-density consensus map for QTLs comparison in B. napus

Increasing seed oil content is one of the most important goals in breeding of rapeseed (B. napus L.). To dissect the genetic basis of oil content in B. napus, a large and new double haploid (DH) population containing 348 lines was obtained from a cross between 'KenC-8' and 'N53-2', two varieties with >10% difference in seed oil content, and this population was named the KN DH population. A genetic linkage map consisting of 403 markers was constructed, which covered a total length of 1783.9 cM with an average marker interval of 4.4 cM. The KN DH population was phenotyped in eight natural environments and subjected to quantitative trait loci (QTL) analysis for oil content. A total of 63 identified QTLs explaining 2.64-17.88% of the phenotypic variation were identified, and these QTLs were further integrated into 24 consensus QTLs located on 11 chromosomes using meta-analysis. A high-density consensus map with 1335 marker loci was constructed by combining the KN DH map with seven other published maps based on the common markers. Of the 24 consensus QTLs in the KN DH population, 14 were new QTLs including five new QTLs in A genome and nine in C genome. The analysis revealed that a larger population with significant differences in oil content gave a higher power detecting new QTLs for oil content, and the construction of the consensus map provided a new clue for comparing the QTLs detected in different populations. These findings enriched our knowledge of QTLs for oil content and should be a potential in marker-assisted breeding of B. napus.

A general framework for association tests with multivariate traits in large-scale genomics studies

Genetic association studies often collect data on multiple traits that are correlated. Discovery of genetic variants influencing multiple traits can lead to better understanding of the etiology of complex human diseases. Conventional univariate association tests may miss variants that have weak or moderate effects on individual traits. We propose several multivariate test statistics to complement univariate tests. Our framework covers both studies of unrelated individuals and family studies and allows any type/mixture of traits. We relate the marginal distributions of multivariate traits to genetic variants and covariates through generalized linear models without modeling the dependence among the traits or family members. We construct score-type statistics, which are computationally fast and numerically stable even in the presence of covariates and which can be combined efficiently across studies with different designs and arbitrary patterns of missing data. We compare the power of the test statistics both theoretically and empirically. We provide a strategy to determine genome-wide significance that properly accounts for the linkage disequilibrium (LD) of genetic variants. The application of the new methods to the meta-analysis of five major cardiovascular cohort studies identifies a new locus (HSCB) that is pleiotropic for the four traits analyzed.

Genetic architecture controlling variation in grain carotenoid composition and concentrations in two maize populations

KEY MESSAGE

Genetic control of maize grain carotenoid profiles is coordinated through several loci distributed throughout three secondary metabolic pathways, most of which exhibit additive, and more importantly, pleiotropic effects. The genetic basis for the variation in maize grain carotenoid concentrations was investigated in two F2:3 populations, DEexp × CI7 and A619 × SC55, derived from high total carotenoid and high β-carotene inbred lines. A comparison of grain carotenoid concentrations from population DEexp × CI7 grown in different environments revealed significantly higher concentrations and greater trait variation in samples harvested from a subtropical environment relative to those from a temperate environment. Genotype by environment interactions was significant for most carotenoid traits. Using phenotypic data in additive, environment-specific genetic models, quantitative trait loci (QTL) were identified for absolute and derived carotenoid traits in each population, including those specific to the isomerization of β-carotene. A multivariate approach for these correlated traits was taken, using carotenoid trait principal components (PCs) that jointly accounted for 97 % or more of trait variation. Component loadings for carotenoid PCs were interpreted in the context of known substrate-product relationships within the carotenoid pathway. Importantly, QTL for univariate and multivariate traits were found to cluster in close proximity to map locations of loci involved in methyl-erythritol, isoprenoid and carotenoid metabolism. Several of these genes, including lycopene epsilon cyclase, carotenoid cleavage dioxygenase1 and beta-carotene hydroxylase, were mapped in the segregating populations. These loci exhibited pleiotropic effects on α-branch carotenoids, total carotenoid profile and β-branch carotenoids, respectively. Our results confirm that several QTL are involved in the modification of carotenoid profiles, and suggest genetic targets that could be used for the improvement of total carotenoid and β-carotene in future breeding populations.

Multi-trait and multi-environment QTL analyses of yield and a set of physiological traits in pepper

A mixed model framework was defined for QTL analysis of multiple traits across multiple environments for a RIL population in pepper. Detection power for QTLs increased considerably and detailed study of QTL by environment interactions and pleiotropy was facilitated. For many agronomic crops, yield is measured simultaneously with other traits across multiple environments. The study of yield can benefit from joint analysis with other traits and relations between yield and other traits can be exploited to develop indirect selection strategies. We compare the performance of three multi-response QTL approaches based on mixed models: a multi-trait approach (MT), a multi-environment approach (ME), and a multi-trait multi-environment approach (MTME). The data come from a multi-environment experiment in pepper, for which 15 traits were measured in four environments. The approaches were compared in terms of number of QTLs detected for each trait, the explained variance, and the accuracy of prediction for the final QTL model. For the four environments together, the superior MTME approach delivered a total of 47 regions containing putative QTLs. Many of these QTLs were pleiotropic and showed quantitative QTL by environment interaction. MTME was superior to ME and MT in the number of QTLs, the explained variance and accuracy of predictions. The large number of model parameters in the MTME approach was challenging and we propose several guidelines to help obtain a stable final QTL model. The results confirmed the feasibility and strengths of novel mixed model QTL methodology to study the architecture of complex traits.

Multi-trait and multi-environment QTL analyses of yield and a set of physiological traits in pepper

A mixed model framework was defined for QTL analysis of multiple traits across multiple environments for a RIL population in pepper. Detection power for QTLs increased considerably and detailed study of QTL by environment interactions and pleiotropy was facilitated. For many agronomic crops, yield is measured simultaneously with other traits across multiple environments. The study of yield can benefit from joint analysis with other traits and relations between yield and other traits can be exploited to develop indirect selection strategies. We compare the performance of three multi-response QTL approaches based on mixed models: a multi-trait approach (MT), a multi-environment approach (ME), and a multi-trait multi-environment approach (MTME). The data come from a multi-environment experiment in pepper, for which 15 traits were measured in four environments. The approaches were compared in terms of number of QTLs detected for each trait, the explained variance, and the accuracy of prediction for the final QTL model. For the four environments together, the superior MTME approach delivered a total of 47 regions containing putative QTLs. Many of these QTLs were pleiotropic and showed quantitative QTL by environment interaction. MTME was superior to ME and MT in the number of QTLs, the explained variance and accuracy of predictions. The large number of model parameters in the MTME approach was challenging and we propose several guidelines to help obtain a stable final QTL model. The results confirmed the feasibility and strengths of novel mixed model QTL methodology to study the architecture of complex traits.

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.

Selecting informative traits for multivariate quantitative trait locus mapping helps to gain optimal power

A major consideration in multitrait analysis is which traits should be jointly analyzed. As a common strategy, multitrait analysis is performed either on pairs of traits or on all of traits. To fully exploit the power of multitrait analysis, we propose variable selection to choose a subset of informative traits for multitrait quantitative trait locus (QTL) mapping. The proposed method is very useful for achieving optimal statistical power for QTL identification and for disclosing the most relevant traits. It is also a practical strategy to effectively take advantage of multitrait analysis when the number of traits under consideration is too large, making the usual multivariate analysis of all traits challenging. We study the impact of selection bias and the usage of permutation tests in the context of variable selection and develop a powerful implementation procedure of variable selection for genome scanning. We demonstrate the proposed method and selection procedure in a backcross population, using both simulated and real data. The extension to other experimental mapping populations is straightforward.

A Bayesian nonparametric approach for mapping dynamic quantitative traits

In biology, many quantitative traits are dynamic in nature. They can often be described by some smooth functions or curves. A joint analysis of all the repeated measurements of the dynamic traits by functional quantitative trait loci (QTL) mapping methods has the benefits to (1) understand the genetic control of the whole dynamic process of the quantitative traits and (2) improve the statistical power to detect QTL. One crucial issue in functional QTL mapping is how to correctly describe the smoothness of trajectories of functional valued traits. We develop an efficient Bayesian nonparametric multiple-loci procedure for mapping dynamic traits. The method uses the Bayesian P-splines with (nonparametric) B-spline bases to specify the functional form of a QTL trajectory and a random walk prior to automatically determine its degree of smoothness. An efficient deterministic variational Bayes algorithm is used to implement both (1) the search of an optimal subset of QTL among large marker panels and (2) estimation of the genetic effects of the selected QTL changing over time. Our method can be fast even on some large-scale data sets. The advantages of our method are illustrated on both simulated and real data sets.

Mapping of QTLs associated with biological nitrogen fixation traits in soybean

Biological nitrogen fixation (BNF) is a key process, but despite the economic and environmental importance, few studies about quantitative trait loci (QTL) controlling BNF traits are available, even in the economically important crop soybean Glycine max (L.) Merr. In this study, a population of 157 F2:7 RILs derived from crossing soybean cultivars Bossier (high BNF capacity) and Embrapa 20 (medium BNF capacity) was genotyped with 105 simple sequence repeat markers (SSRs). The genetic map obtained has 1231.2 cM and covers about 50% of the genome, with an average interval of 18.1 cM. Three traits, nodule number (NN), the ratio nodule dry weight (NDW)/NN and shoot dry weight (SDW) were used to evaluate BNF performance. A composite interval mapping for multiple traits method (mCIM) analysis mapped two QTLs for SDW (LGs E and L), three for NN (LGs B1, E and I), and one for NDW/NN (LG I); all QTLs were of small effect (R-values ranging from 1.7% to 10.0%) and explained 15.4%, 13.8% and 6.5% of total variation for these three traits, respectively.

The genetic basis of speciation in the Giliopsis lineage of Ipomopsis (Polemoniaceae)

One of the most powerful drivers of speciation in plants is pollinator-mediated disruptive selection, which leads to the divergence of floral traits adapted to the morphology and behavior of different pollinators. Despite the widespread importance of this speciation mechanism, its genetic basis has been explored in only a few groups. Here, we characterize the genetic basis of pollinator-mediated divergence of two species in genus Ipomopsis, I. guttata and I. tenuifolia, using quantitative trait locus (QTL) analyses of floral traits and other variable phenotypes. We detected one to six QTLs per trait, with each QTL generally explaining small to modest amounts of the phenotypic variance of a backcross hybrid population. In contrast, flowering time and anthocyanin abundance (a metric of color variation) were controlled by a few QTLs of relatively large effect. QTLs were strongly clustered within linkage groups, with 26 of 37 QTLs localized to six marker-interval 'hotspots,' all of which harbored pleiotropic QTLs. In contrast to other studies that have examined the genetic basis of pollinator shifts, our results indicate that, in general, mutations of small to modest effect on phenotype were involved. Thus, the evolutionary transition between the distinct pollination modes of I. guttata and I. tenuifolia likely proceeded incrementally, rather than saltationally.

The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis

Genotype-by-environment interaction (GEI) is an important phenomenon in plant breeding. This paper presents a series of models for describing, exploring, understanding, and predicting GEI. All models depart from a two-way table of genotype by environment means. First, a series of descriptive and explorative models/approaches are presented: Finlay–Wilkinson model, AMMI model, GGE biplot. All of these approaches have in common that they merely try to group genotypes and environments and do not use other information than the two-way table of means. Next, factorial regression is introduced as an approach to explicitly introduce genotypic and environmental covariates for describing and explaining GEI. Finally, QTL modeling is presented as a natural extension of factorial regression, where marker information is translated into genetic predictors. Tests for regression coefficients corresponding to these genetic predictors are tests for main effect QTL expression and QTL by environment interaction (QEI). QTL models for which QEI depends on environmental covariables form an interesting model class for predicting GEI for new genotypes and new environments. For realistic modeling of genotypic differences across multiple environments, sophisticated mixed models are necessary to allow for heterogeneity of genetic variances and correlations across environments. The use and interpretation of all models is illustrated by an example data set from the CIMMYT maize breeding program, containing environments differing in drought and nitrogen stress. To help readers to carry out the statistical analyses, GenStat® programs, 15th Edition and Discovery® version, are presented as “Appendix.”

Accuracy of across-environment genome-wide prediction in maize nested association mapping populations

Most of previous empirical studies with genome-wide prediction were focused on within-environment prediction based on a single-environment (SE) model. In this study, we evaluated accuracy improvements of across-environment prediction by using genetic and residual covariance across correlated environments. Predictions with a multienvironment (ME) model were evaluated for two corn polygenic leaf structure traits, leaf length and leaf width, based on within-population (WP) and across-population (AP) experiments using a large maize nested association mapping data set consisting of 25 populations of recombinant inbred-lines. To make our study more applicable to plant breeding, two cross-validation schemes were used by evaluating accuracies of (CV1) predicting unobserved phenotypes of untested lines and (CV2) predicting unobserved phenotypes of lines that have been evaluated in some environments but not others. We concluded that (1) genome-wide prediction provided greater prediction accuracies than traditional quantitative trait loci-based prediction in both WP and AP and provided more advantages over quantitative trait loci -based prediction for WP than for AP. (2) Prediction accuracy with ME was significantly greater than that attained by SE in CV1 and CV2, and gains with ME over SE were greater in CV2 than in CV1. These gains were also greater in WP than in AP in both CV1 and CV2. (3) Gains with ME over SE attributed to genetic correlation between environments, with little effect from residual correlation. Impacts of marker density on predictions also were investigated in this study.

Identification of the quantitative trait loci (QTL) underlying water soluble protein content in soybean

Water soluble protein content (SPC) plays an important role in the functional efficacy of protein in food products. Therefore, for the identification of quantitative trait loci (QTL) associated with SPC, 212 F(2:9) lines of the recombinant inbred line (RIL) population derived from the cross of ZDD09454 × Yudou12 were grown along with the parents, in six different environments (location × year) to determine inheritance and map solubility-related genes. A linkage map comprising of 301 SSR markers covering 3,576.81 cM was constructed in the RIL population. Seed SPC was quantified with a macro-Kjeldahl procedure in samples collected over multiple years from three locations (Nantong in 2007 and 2008, Zhengzhou in 2007 and 2008, and Xinxiang in 2008 and 2009). SPC demonstrated transgressive segregation, indicating a complementary genetic structure between the parents. Eleven putative QTL were associated with SPC explaining 4.5-18.2 % of the observed phenotypic variation across the 6 year/location environments. Among these, two QTL (qsp8-4, qsp8-5) near GMENOD2B and Sat_215 showed an association with SPC in multiple environments, suggesting that they were key QTL related to protein solubility. The QTL × environment interaction demonstrated the complex genetic mechanism of SPC. These SPC-associated QTL and linked markers in soybean will provide important information that can be utilized by breeders to improve the functional quality of soybean varieties.

Verification of QTL for grain starch content and its genetic correlation with oil content using two connected RIL populations in high-oil maize

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

Verification of QTL for grain starch content and its genetic correlation with oil content using two connected RIL populations in high-oil maize

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

Genetic mapping of a major dominant gene for resistance to Ralstonia solanacearum in eggplant

Resistance of eggplant against Ralstonia solanacearum phylotype I strains was assessed in a F(6) population of recombinant inbred lines (RILs) derived from a intra-specific cross between S. melongena MM738 (susceptible) and AG91-25 (resistant). Resistance traits were determined as disease score, percentage of wilted plants, and stem-based bacterial colonization index, as assessed in greenhouse experiments conducted in Réunion Island, France. The AG91-25 resistance was highly efficient toward strains CMR134, PSS366 and GMI1000, but only partial toward the highly virulent strain PSS4. The partial resistance found against PSS4 was overcome under high inoculation pressure, with heritability estimates from 0.28 to 0.53, depending on the traits and season. A genetic map was built with 119 AFLP, SSR and SRAP markers positioned on 18 linkage groups (LG), for a total length of 884 cM, and used for quantitative trait loci (QTL) analysis. A major dominant gene, named ERs1, controlled the resistance to strains CMR134, PSS366, and GMI1000. Against strain PSS4, this gene was not detected, but a significant QTL involved in delay of disease progress was detected on another LG. The possible use of the major resistance gene ERs1 in marker-assisted selection and the prospects offered for academic studies of a possible gene for gene system controlling resistance to bacterial wilt in solanaceous plants are discussed.

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%).

Pleiotropy and life history evolution in Drosophila melanogaster: uncoupling life span and early fecundity

Populations of Drosophila melanogaster that have been artificially selected for late age of reproduction evolve longer life spans and, in some cases, reduced early fecundity. The negative correlation is widely interpreted as evidence of antagonistic pleiotropy. Here, we show that the correlation breaks down in recombinant genomes. A major quantitative trait locus that increases adult life span by 20% has no detectable effect on early fecundity. Several recombinant genotypes are superflies, exhibiting both elevated early fecundity and long life. The genetic correlation of early fecundity and life span is not different from zero, while the midlife fecundity correlation is positive and statistically significant, suggesting age-specific adaptation. The results are not consistent with a dominant role for negative pleiotropy, but can be understood in terms of a mixture of pleiotropic and recombining nonpleiotropic elements. Life span and early fecundity can be genetically uncoupled.

The many faces of pleiotropy

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

Mapping the genetic basis of symbiotic variation in legume-rhizobium interactions in Medicago truncatula

Mutualisms are known to be genetically variable, where the genotypes differ in the fitness benefits they gain from the interaction. To date, little is known about the loci that underlie such genetic variation in fitness or whether the loci influencing fitness are partner specific, and depend on the genotype of the interaction partner. In the legume-rhizobium mutualism, one set of potential candidate genes that may influence the fitness benefits of the symbiosis are the plant genes involved in the initiation of the signaling pathway between the two partners. Here we performed quantitative trait loci (QTL) mapping in Medicago truncatula in two different rhizobium strain treatments to locate regions of the genome influencing plant traits, assess whether such regions are dependent on the genotype of the rhizobial mutualist (QTL × rhizobium strain), and evaluate the contribution of sequence variation at known symbiosis signaling genes. Two of the symbiotic signaling genes, NFP and DMI3, colocalized with two QTL affecting average fruit weight and leaf number, suggesting that natural variation in nodulation genes may potentially influence plant fitness. In both rhizobium strain treatments, there were QTL that influenced multiple traits, indicative of either tight linkage between loci or pleiotropy, including one QTL with opposing effects on growth and reproduction. There was no evidence for QTL × rhizobium strain or genotype × genotype interactions, suggesting either that such interactions are due to small-effect loci or that more genotype-genotype combinations need to be tested in future mapping studies.

Multiple-trait genomic selection methods increase genetic value prediction accuracy

Genetic correlations between quantitative traits measured in many breeding programs are pervasive. These correlations indicate that measurements of one trait carry information on other traits. Current single-trait (univariate) genomic selection does not take advantage of this information. Multivariate genomic selection on multiple traits could accomplish this but has been little explored and tested in practical breeding programs. In this study, three multivariate linear models (i.e., GBLUP, BayesA, and BayesCπ) were presented and compared to univariate models using simulated and real quantitative traits controlled by different genetic architectures. We also extended BayesA with fixed hyperparameters to a full hierarchical model that estimated hyperparameters and BayesCπ to impute missing phenotypes. We found that optimal marker-effect variance priors depended on the genetic architecture of the trait so that estimating them was beneficial. We showed that the prediction accuracy for a low-heritability trait could be significantly increased by multivariate genomic selection when a correlated high-heritability trait was available. Further, multiple-trait genomic selection had higher prediction accuracy than single-trait genomic selection when phenotypes are not available on all individuals and traits. Additional factors affecting the performance of multiple-trait genomic selection were explored.

Multivariate whole genome average interval mapping: QTL analysis for multiple traits and/or environments

A major aim in some plant-based studies is the determination of quantitative trait loci (QTL) for multiple traits or across multiple environments. Understanding these QTL by trait or QTL by environment interactions can be of great value to the plant breeder. A whole genome approach for the analysis of QTL is presented for such multivariate applications. The approach is an extension of whole genome average interval mapping in which all intervals on a linkage map are included in the analysis simultaneously. A random effects working model is proposed for the multivariate (trait or environment) QTL effects for each interval, with a variance-covariance matrix linking the variates in a particular interval. The significance of the variance-covariance matrix for the QTL effects is tested and if significant, an outlier detection technique is used to select a putative QTL. This QTL by variate interaction is transferred to the fixed effects. The process is repeated until the variance-covariance matrix for QTL random effects is not significant; at this point all putative QTL have been selected. Unlinked markers can also be included in the analysis. A simulation study was conducted to examine the performance of the approach and demonstrated the multivariate approach results in increased power for detecting QTL in comparison to univariate methods. The approach is illustrated for data arising from experiments involving two doubled haploid populations. The first involves analysis of two wheat traits, α-amylase activity and height, while the second is concerned with a multi-environment trial for extensibility of flour dough. The method provides an approach for multi-trait and multi-environment QTL analysis in the presence of non-genetic sources of variation.

A mixed-model approach for genome-wide association studies of correlated traits in structured populations

Genome-wide association studies (GWAS) are a standard approach for studying the genetics of natural variation. A major concern in GWAS is the need to account for the complicated dependence-structure of the data both between loci as well as between individuals. Mixed models have emerged as a general and flexible approach for correcting for population structure in GWAS. Here we extend this linear mixed model approach to carry out GWAS of correlated phenotypes, deriving a fully parameterized multi-trait mixed model (MTMM) that considers both the within-trait and between-trait variance components simultaneously for multiple traits. We apply this to human cohort data for correlated blood lipid traits from the Northern Finland Birth Cohort 1966, and demonstrate greatly increased power to detect pleiotropic loci that affect more than one blood lipid trait. We also apply this to an Arabidopsis dataset for flowering measurements in two different locations, identifying loci whose effect depends on the environment.

Dynamic semiparametric Bayesian models for genetic mapping of complex trait with irregular longitudinal data

Many phenomena of fundamental importance to biology and biomedicine arise as a dynamic curve, such as organ growth and HIV dynamics. The genetic mapping of these traits is challenged by longitudinal variables measured at irregular and possibly subject-specific time points, in which case nonnegative definiteness of the estimated covariance matrix needs to be guaranteed. We present a semiparametric approach for genetic mapping within the mixture-model setting by jointly modeling mean and covariance structures for irregular longitudinal data. Penalized spline is used to model the mean functions of individual quantitative trait locus (QTL) genotypes as latent variables, whereas an extended generalized linear model is used to approximate the covariance matrix. The parameters for modeling the mean-covariances are estimated by MCMC, using the Gibbs sampler and the Metropolis-Hastings algorithm. We derive the full conditional distributions for the mean and covariance parameters and compute Bayes factors to test the hypothesis about the existence of significant QTLs. We used the model to screen the existence of specific QTLs for age-specific change of body mass index with a sparse longitudinal data set. The new model provides powerful means for broadening the application of genetic mapping to reveal the genetic control of dynamic traits.

Multiple trait multiple interval mapping of quantitative trait loci from inbred line crosses

Background

Although many experiments have measurements on multiple traits, most studies performed the analysis of mapping of quantitative trait loci (QTL) for each trait separately using single trait analysis. Single trait analysis does not take advantage of possible genetic and environmental correlations between traits. In this paper, we propose a novel statistical method for multiple trait multiple interval mapping (MTMIM) of QTL for inbred line crosses. We also develop a novel score-based method for estimating genome-wide significance level of putative QTL effects suitable for the MTMIM model. The MTMIM method is implemented in the freely available and widely used Windows QTL Cartographer software.

Results

Throughout the paper, we provide compelling empirical evidences that: (1) the score-based threshold maintains proper type I error rate and tends to keep false discovery rate within an acceptable level; (2) the MTMIM method can deliver better parameter estimates and power than single trait multiple interval mapping method; (3) an analysis of Drosophila dataset illustrates how the MTMIM method can better extract information from datasets with measurements in multiple traits.

Conclusions

The MTMIM method represents a convenient statistical framework to test hypotheses of pleiotropic QTL versus closely linked nonpleiotropic QTL, QTL by environment interaction, and to estimate the total genotypic variance-covariance matrix between traits and to decompose it in terms of QTL-specific variance-covariance matrices, therefore, providing more details on the genetic architecture of complex traits.

Bayesian mapping of multiple traits in maize: the importance of pleiotropic effects in studying the inheritance of quantitative traits

Pleiotropy has played an important role in understanding quantitative traits. However, the extensiveness of this effect in the genome and its consequences for plant improvement have not been fully elucidated. The aim of this study was to identify pleiotropic quantitative trait loci (QTLs) in maize using Bayesian multiple interval mapping. Additionally, we sought to obtain a better understanding of the inheritance, extent and distribution of pleiotropic effects of several components in maize production. The design III procedure was used from a population derived from the cross of the inbred lines L-14-04B and L-08-05F. Two hundred and fifty plants were genotyped with 177 microsatellite markers and backcrossed to both parents giving rise to 500 backcrossed progenies, which were evaluated in six environments for grain yield and its components. The results of this study suggest that mapping isolated traits limits our understanding of the genetic architecture of quantitative traits. This architecture can be better understood by using pleiotropic networks that facilitate the visualization of the complexity of quantitative inheritance, and this characterization will help to develop new selection strategies. It was also possible to confront the idea that it is feasible to identify QTLs for complex traits such as grain yield, as pleiotropy acts prominently on its subtraits and as this "trait" can be broken down and predicted almost completely by the QTLs of its components. Additionally, pleiotropic QTLs do not necessarily signify pleiotropy of allelic interactions, and this indicates that the pervasive pleiotropy does not limit the genetic adaptability of plants.

Genotype by environment interaction of quantitative traits: a case study in barley

Genotype by environment interaction is a phenomenon that a better genotype in one environment may perform poorly in another environment. When the genotype refers to a quantitative trait locus (QTL), this phenomenon is called QTL by environment interaction, denoted by Q×E. Using a recently developed new Bayesian method and genome-wide marker information, we estimated and tested QTL main effects and Q×E interactions for a well-known barley dataset produced by the North American Barley Genome Mapping Project. This dataset contained seven quantitative traits collected from 145 doubled-haploid (DH) lines evaluated in multiple environments, which derived from a cross between two Canadian two-row barley lines, Harrington and TR306. Numerous main effects and Q×E interaction effects have been detected for all seven quantitative traits. However, main effects seem to be more important than the Q×E interaction effects for all seven traits examined. The number of main effects detected varied from 26 for the maturity trait to 75 for the heading trait, with an average of 61.86. The heading trait has the most detected effects, with a total of 98 (75 main, 29 Q×E). Among the 98 effects, 6 loci had both the main and Q×E effects. Among the total number of detected loci, on average, 78.5% of the loci show the main effects whereas 34.9% of the loci show Q×E interactions. Overall, we detected many loci with either the main or the Q×E effects, and the main effects appear to be more important than the Q×E interaction effects for all the seven traits. This means that most detected loci have a constant effect across environments. Another discovery from this analysis is that Q×E interaction occurs independently, regardless whether the locus has main effects.

Survival of heat stress with and without heat hardening in Drosophila melanogaster: interactions with larval density

Survival of a potentially lethal high temperature stress is a genetically variable thermal adaptation trait in many organisms. Organisms cope with heat stress by basal or induced thermoresistance. Here, we tested quantitative trait loci (QTL) for heat stress survival (HSS) in Drosophila melanogaster, with and without a cyclic heat-hardening pre-treatment, for flies that were reared at low (LD) or high (HD) density. Mapping populations were two panels of recombinant inbred lines (RIL), which were previously constructed from heat stress-selected stocks: RIL-D48 and RIL-SH2, derived from backcrosses to stocks of low and high heat resistance, respectively. HSS increased with heat hardening in both LD and HD flies. In addition, HSS increased consistently with density in non-hardened flies. There was a significant interaction between heat hardening and density effects in RIL-D48. Several QTL were significant for both density and hardening treatments. Many QTL overlapped with thermotolerance QTL identified for other traits in previous studies based on LD cultures only. However, three new QTL were found in HD only (cytological ranges: 12E–16F6; 30A3–34C2; 49C–50C). Previously found thermotolerance QTL were also significant for flies from HD cultures.

Mapping of angular leaf spot resistance QTL in common bean (Phaseolus vulgaris L.) under different environments

BACKGROUND

Common bean (Phaseolus vulgaris L.) is the most important grain legume for human diet worldwide and the angular leaf spot (ALS) is one of the most devastating diseases of this crop, leading to yield losses as high as 80%. In an attempt to breed resistant cultivars, it is important to first understand the inheritance mode of resistance and to develop tools that could be used in assisted breeding. Therefore, the aim of this study was to identify quantitative trait loci (QTL) controlling resistance to ALS under natural infection conditions in the field and under inoculated conditions in the greenhouse.

RESULTS

QTL analyses were made using phenotypic data from 346 recombinant inbreed lines from the IAC-UNAxCAL 143 cross, gathered in three experiments, two of which were conducted in the field in different seasons and one in the greenhouse. Joint composite interval mapping analysis of QTLxenvironment interaction was performed. In all, seven QTLs were mapped on five linkage groups. Most of them, with the exception of two, were significant in all experiments. Among these, ALS10.1DG,UC presented major effects (R2 between 16%-22%). This QTL was found linked to the GATS11b marker of linkage group B10, which was consistently amplified across a set of common bean lines and was associated with the resistance. Four new QTLs were identified. Between them the ALS5.2 showed an important effect (9.4%) under inoculated conditions in the greenhouse. ALS4.2 was another major QTL, under natural infection in the field, explaining 10.8% of the variability for resistance reaction. The other QTLs showed minor effects on resistance.

CONCLUSIONS

The results indicated a quantitative inheritance pattern of ALS resistance in the common bean line CAL 143. QTL x environment interactions were observed. Moreover, the major QTL identified on linkage group B10 could be important for bean breeding, as it was stable in all the environments. Thereby, the GATS11b marker is a potential tool for marker assisted selection for ALS resistance.

Genetic basis of local adaptation and flowering time variation in Arabidopsis lyrata

Understanding how genetic variation at individual loci contributes to adaptation of populations to different local environments is an important topic in modern evolutionary biology. To date, most evidence has pointed to conditionally neutral quantitative trait loci (QTL) showing fitness effects only in some environments, while there has been less evidence for single-locus fitness trade-offs. At QTL underlying local adaptation, alleles from the local population are expected to show a fitness advantage. Cytoplasmic genomes also can have a role in local adaptation, but the role of cytonuclear interactions in adaptive differentiation has remained largely unknown. We mapped genomic regions underlying adaptive differentiation in multiple fitness components and flowering time in diverged populations of a perennial plant Arabidopsis lyrata. Experimental hybrids for this purpose were grown in natural field conditions of the parental populations in Norway and North Carolina (NC), USA, and in the greenhouse. We found QTL where high fitness and early flowering were associated with local alleles, indicating a role of different selection pressures in phenotypic differentiation. At two QTL regions, a fitness component showing local adaptation between the parental populations also showed signs of putative fitness trade-offs. Beneficial dominance effects of conditionally neutral QTL for different fitness components resulted in hybrid vigour at the Norwegian site in the F(2) hybrids. We also found that cytoplasmic genomes contributed to local adaptation and hybrid vigour by interacting with nuclear QTL, but these interactions did not show evidence for cytonuclear coadaptation (high fitness of local alleles combined with the local cytoplasm).

A hierarchical bayesian approach to multi-trait clinical quantitative trait locus modeling

Recent advances in high-throughput genotyping and transcript profiling technologies have enabled the inexpensive production of genome-wide dense marker maps in tandem with huge amounts of expression profiles. These large-scale data encompass valuable information about the genetic architecture of important phenotypic traits. Comprehensive models that combine molecular markers and gene transcript levels are increasingly advocated as an effective approach to dissecting the genetic architecture of complex phenotypic traits. The simultaneous utilization of marker and gene expression data to explain the variation in clinical quantitative trait, known as clinical quantitative trait locus (cQTL) mapping, poses challenges that are both conceptual and computational. Nonetheless, the hierarchical Bayesian (HB) modeling approach, in combination with modern computational tools such as Markov chain Monte Carlo (MCMC) simulation techniques, provides much versatility for cQTL analysis. Sillanpää and Noykova (2008) developed a HB model for single-trait cQTL analysis in inbred line cross-data using molecular markers, gene expressions, and marker-gene expression pairs. However, clinical traits generally relate to one another through environmental correlations and/or pleiotropy. A multi-trait approach can improve on the power to detect genetic effects and on their estimation precision. A multi-trait model also provides a framework for examining a number of biologically interesting hypotheses. In this paper we extend the HB cQTL model for inbred line crosses proposed by Sillanpää and Noykova to a multi-trait setting. We illustrate the implementation of our new model with simulated data, and evaluate the multi-trait model performance with regard to its single-trait counterpart. The data simulation process was based on the multi-trait cQTL model, assuming three traits with uncorrelated and correlated cQTL residuals, with the simulated data under uncorrelated cQTL residuals serving as our test set for comparing the performances of the multi-trait and single-trait models. The simulated data under correlated cQTL residuals were essentially used to assess how well our new model can estimate the cQTL residual covariance structure. The model fitting to the data was carried out by MCMC simulation through OpenBUGS. The multi-trait model outperformed its single-trait counterpart in identifying cQTLs, with a consistently lower false discovery rate. Moreover, the covariance matrix of cQTL residuals was typically estimated to an appreciable degree of precision under the multi-trait cQTL model, making our new model a promising approach to addressing a wide range of issues facing the analysis of correlated clinical traits.

Genetic mapping of paternal sorting of mitochondria in cucumber

Mitochondria are organelles that have their own DNA; serve as the powerhouses of eukaryotic cells; play important roles in stress responses, programmed cell death, and ageing; and in the vast majority of eukaryotes, are maternally transmitted. Strict maternal transmission of mitochondria makes it difficult to select for better-performing mitochondria, or against deleterious mutations in the mitochondrial DNA. Cucumber is a useful plant for organellar genetics because its mitochondria are paternally transmitted and it possesses one of the largest mitochondrial genomes among all eukaryotes. Recombination among repetitive motifs in the cucumber mitochondrial DNA produces rearrangements associated with strongly mosaic (MSC) phenotypes. We previously reported nuclear control of sorting among paternally transmitted mitochondrial DNAs. The goal of this project was to map paternal sorting of mitochondria as a step towards its eventual cloning. We crossed single plants from plant introduction (PI) 401734 and Cucumis sativus var. hardwickii and produced an F(2) family. A total of 425 F(2) plants were genotyped for molecular markers and testcrossed as the female with MSC16. Testcross families were scored for frequencies of wild-type versus MSC progenies. Discrete segregations for percent wild-type progenies were not observed and paternal sorting of mitochondria was therefore analyzed as a quantitative trait. A major quantitative trait locus (QTL; LOD >23) was mapped between two simple sequence repeats encompassing a 459-kb region on chromosome 3. Nuclear genes previously shown to affect the prevalence of mitochondrial DNAs (MSH1, OSB1, and RECA homologs) were not located near this major QTL on chromosome 3. Sequencing of this region from PI 401734, together with improved annotation of the cucumber genome, should result in the eventual cloning of paternal sorting of mitochondria and provide insights about nuclear control of organellar-DNA sorting.

Identifying QTL for multiple complex traits in experimental crosses

In this chapter, we consider the problem of jointly analyzing multiple (correlated) complex traits in the context of identifying quantitative trait loci (QTL). The advantages of joint analysis (as opposed independent analysis) is the detection of pleiotropy and improved precision of estimates. The multivariate model is introduced along with a brief description of the setup. The model is evaluated in a Bayesian framework to perform model selection (strategy to identify QTL for each trait). A detailed vignette of a statistical software (R/qtlbim) which uses a Markov Chain Monte Carlo (MCMC) approach to draw samples from the posterior distribution is presented. Strategies of checking MCMC convergence, visualization of posterior samples, model building, and testing pleiotropy with the software are described. Relevant code to perform the analysis on an example (simulated) dataset is also provided.

Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis

A comprehensive and large-scale metabolome quantitative trait loci (mQTL) analysis was performed to investigate the genetic backgrounds associated with metabolic phenotypes in rice grains. The metabolome dataset consisted of 759 metabolite signals obtained from the grains of 85 lines of rice (Oryza sativa, Sasanishiki × Habataki back-crossed inbred lines). Metabolome analysis was performed using four mass spectrometry pipelines to enhance detection of different classes of metabolites. This mQTL analysis of a wide range of metabolites highlighted an uneven distribution of 802 mQTLs on the rice genome, as well as different modes of metabolic trait (m-trait) control among various types of metabolites. The levels of most metabolites within rice grains were highly sensitive to environmental factors, but only weakly associated with mQTLs. Coordinated control was observed for several groups of metabolites, such as amino acids linked to the mQTL hotspot on chromosome 3. For flavonoids, m-trait variation among the experimental lines was tightly governed by genetic factors that alter the glycosylation of flavones. Many loci affecting levels of metabolites were detected by QTL analysis, and plausible gene candidates were evaluated by in silico analysis. Several mQTLs profoundly influenced metabolite levels, providing insight into the control of rice metabolism. The genomic region and genes potentially responsible for the biosynthesis of apigenin-6,8-di-C-α-l-arabinoside are presented as an example of a critical mQTL identified by the analysis.

Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation

DNA variation can be used as a systematic source of perturbation in segregating populations as a way to infer regulatory networks via the integration of large-scale, high-dimensional molecular profiling data.

Cells employ multiple levels of regulation, including transcriptional and translational regulation, that drive core biological processes and enable cells to respond to genetic and environmental changes. Small-molecule metabolites are one category of critical cellular intermediates that can influence as well as be a target of cellular regulations. Because metabolites represent the direct output of protein-mediated cellular processes, endogenous metabolite concentrations can closely reflect cellular physiological states, especially when integrated with other molecular-profiling data. Here we develop and apply a network reconstruction approach that simultaneously integrates six different types of data: endogenous metabolite concentration, RNA expression, DNA variation, DNA–protein binding, protein–metabolite interaction, and protein–protein interaction data, to construct probabilistic causal networks that elucidate the complexity of cell regulation in a segregating yeast population. Because many of the metabolites are found to be under strong genetic control, we were able to employ a causal regulator detection algorithm to identify causal regulators of the resulting network that elucidated the mechanisms by which variations in their sequence affect gene expression and metabolite concentrations. We examined all four expression quantitative trait loci (eQTL) hot spots with colocalized metabolite QTLs, two of which recapitulated known biological processes, while the other two elucidated novel putative biological mechanisms for the eQTL hot spots.

It is now possible to score variations in DNA across whole genomes, RNA levels and alternative isoforms, metabolite levels, protein levels and protein state information, protein–protein interactions, and protein–DNA interactions, in a comprehensive fashion in populations of individuals. Interactions among these molecular entities define the complex web of biological processes that give rise to all higher order phenotypes, including disease. The development of analytical approaches that simultaneously integrate different dimensions of data is essential if we are to extract the meaning from large-scale data to elucidate the complexity of living systems. Here, we use a novel Bayesian network reconstruction algorithm that simultaneously integrates DNA variation, RNA levels, metabolite levels, protein–protein interaction data, protein–DNA binding data, and protein–small-molecule interaction data to construct molecular networks in yeast. We demonstrate that these networks can be used to infer causal relationships among genes, enabling the identification of novel genes that modulate cellular regulation. We show that our network predictions either recapitulate known biology or can be prospectively validated, demonstrating a high degree of accuracy in the predicted network.

Lipoprotein Candidate Genes for Multivariate Factors of the Insulin Resistance Syndrome: A Sib-pair Linkage Analysis in Women Twins

The insulin resistance syndrome (IRS) is characterized by a combination of interrelated coronary heart disease risk factors, including low high-density lipoprotein cholesterol (HDLC) levels, obesity and increases in triglyceride (TG), systolic and diastolic blood pressure (BP), small low-density lipoprotein particles (LDL-size), and fasting and postload plasma insulin and glucose. Using factor analysis, we previously identified multivariate factors based on data from women participating in the Kaiser Permanente Women Twins Study: 1) Weight/Fat, 2) Insulin/Glucose, 3) Lipids, and 4) BP. The purpose of this study is to evaluate evidence for genetic linkage between the multivariate factors and candidate genes. Quantitative sib-pair analysis based on the factor scores with markers for 9 candidate genes was carried out based on data from 126 pairs of dizygotic (DZ) women twins from the second exam of the Kaiser Permanente Women Twins study. Suggestive evidence for linkage was found for the Weight/fat factor and the Apo E gene (p= 0.01), and stronger evidence for linkage with the Lipid factor and the cholesterol ester transfer protein (p= 0.002) gene. Therefore, the CETP gene appears to influence covariation in LDL size, TG, and HDL, and may account for a portion of the well-established statistical and metabolic associations observed between these risk factors.

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.

Gene-based multiple trait analysis for exome sequencing data

The common genetic variants identified through genome-wide association studies explain only a small proportion of the genetic risk for complex diseases. The advancement of next-generation sequencing technologies has enabled the detection of rare variants that are expected to contribute significantly to the missing heritability. Some genetic association studies provide multiple correlated traits for analysis. Multiple trait analysis has the potential to improve the power to detect pleiotropic genetic variants that influence multiple traits. We propose a gene-level association test for multiple traits that accounts for correlation among the traits. Gene- or region-level testing for association involves both common and rare variants. Statistical tests for common variants may have limited power for individual rare variants because of their low frequency and multiple testing issues. To address these concerns, we use the weighted-sum pooling method to test the joint association of multiple rare and common variants within a gene. The proposed method is applied to the Genetic Association Workshop 17 (GAW17) simulated mini-exome data to analyze multiple traits. Because of the nature of the GAW17 simulation model, increased power was not observed for multiple-trait analysis compared to single-trait analysis. However, multiple-trait analysis did not result in a substantial loss of power because of the testing of multiple traits. We conclude that this method would be useful for identifying pleiotropic genes.

Joint association analysis of bivariate quantitative and qualitative traits

Univariate genome-wide association analysis of quantitative and qualitative traits has been investigated extensively in the literature. In the presence of correlated phenotypes, it is more intuitive to analyze all phenotypes simultaneously. We describe an efficient likelihood-based approach for the joint association analysis of quantitative and qualitative traits in unrelated individuals. We assume a probit model for the qualitative trait, under which an unobserved latent variable and a prespecified threshold determine the value of the qualitative trait. To jointly model the quantitative and qualitative traits, we assume that the quantitative trait and the latent variable follow a bivariate normal distribution. The latent variable is allowed to be correlated with the quantitative phenotype. Simultaneous modeling of the quantitative and qualitative traits allows us to make more precise inference on the pleiotropic genetic effects. We derive likelihood ratio tests for the testing of genetic effects. An application to the Genetic Analysis Workshop 17 data is provided. The new method yields reasonable power and meaningful results for the joint association analysis of the quantitative trait Q1 and the qualitative trait disease status at SNPs with not too small MAF.

Genetic signal maximization using environmental regression

Joint analyses of correlated phenotypes in genetic epidemiology studies are common. However, these analyses primarily focus on genetic correlation between traits and do not take into account environmental correlation. We describe a method that optimizes the genetic signal by accounting for stochastic environmental noise through joint analysis of a discrete trait and a correlated quantitative marker. We conducted bivariate analyses where heritability and the environmental correlation between the discrete and quantitative traits were calculated using Genetic Analysis Workshop 17 (GAW17) family data. The resulting inverse value of the environmental correlation between these traits was then used to determine a new β coefficient for each quantitative trait and was constrained in a univariate model. We conducted genetic association tests on 7,087 nonsynonymous SNPs in three GAW17 family replicates for Affected status with the β coefficient fixed for three quantitative phenotypes and compared these to an association model where the β coefficient was allowed to vary. Bivariate environmental correlations were 0.64 (± 0.09) for Q1, 0.798 (± 0.076) for Q2, and −0.169 (± 0.18) for Q4. Heritability of Affected status improved in each univariate model where a constrained β coefficient was used to account for stochastic environmental effects. No genome-wide significant associations were identified for either method but we demonstrated that constraining β for covariates slightly improved the genetic signal for Affected status. This environmental regression approach allows for increased heritability when the β coefficient for a highly correlated quantitative covariate is constrained and increases the genetic signal for the discrete trait.

A phenomics-based strategy identifies loci on APOC1, BRAP, and PLCG1 associated with metabolic syndrome phenotype domains

Despite evidence of the clustering of metabolic syndrome components, current approaches for identifying unifying genetic mechanisms typically evaluate clinical categories that do not provide adequate etiological information. Here, we used data from 19,486 European American and 6,287 African American Candidate Gene Association Resource Consortium participants to identify loci associated with the clustering of metabolic phenotypes. Six phenotype domains (atherogenic dyslipidemia, vascular dysfunction, vascular inflammation, pro-thrombotic state, central obesity, and elevated plasma glucose) encompassing 19 quantitative traits were examined. Principal components analysis was used to reduce the dimension of each domain such that >55% of the trait variance was represented within each domain. We then applied a statistically efficient and computational feasible multivariate approach that related eight principal components from the six domains to 250,000 imputed SNPs using an additive genetic model and including demographic covariates. In European Americans, we identified 606 genome-wide significant SNPs representing 19 loci. Many of these loci were associated with only one trait domain, were consistent with results in African Americans, and overlapped with published findings, for instance central obesity and FTO. However, our approach, which is applicable to any set of interval scale traits that is heritable and exhibits evidence of phenotypic clustering, identified three new loci in or near APOC1, BRAP, and PLCG1, which were associated with multiple phenotype domains. These pleiotropic loci may help characterize metabolic dysregulation and identify targets for intervention.