Genetic architecture of highly complex chemical resistance traits across four yeast strains

Many questions about the genetic basis of complex traits remain unanswered. This is in part due to the low statistical power of traditional genetic mapping studies. We used a statistically powerful approach, extreme QTL mapping (X-QTL), to identify the genetic basis of resistance to 13 chemicals in all 6 pairwise crosses of four ecologically and genetically diverse yeast strains, and we detected a total of more than 800 loci. We found that the number of loci detected in each experiment was primarily a function of the trait (explaining 46% of the variance) rather than the cross (11%), suggesting that the level of genetic complexity is a consistent property of a trait across different genetic backgrounds. Further, we observed that most loci had trait-specific effects, although a small number of loci with effects in many conditions were identified. We used the patterns of resistance and susceptibility alleles in the four parent strains to make inferences about the allele frequency spectrum of functional variants. We also observed evidence of more complex allelic series at a number of loci, as well as strain-specific signatures of selection. These results improve our understanding of complex traits in yeast and have implications for study design in other organisms.     

Genetic architecture of leaf ecophysiological traits in Helianthus

We investigated quantitative trait loci (QTLs) for several leaf chemistry traits in early-generation hybrids between Helianthus annuus and Helianthus petiolaris, the parental species of the ancient diploid hybrid sunflower species Helianthus anomalus, Helianthus deserticola, and Helianthus paradoxus. We grew individuals of a second-generation backcross (BC(2)) toward H. petiolaris under optimum conditions in a glass house experiment. Trait values were measured once for each individual. In addition, genotypic data previously determined for each individual were employed for composite interval mapping of QTLs. We detected QTLs for leaf carbon concentration, leaf nitrogen concentration, leaf nitrogen per unit area, and photosynthetic nitrogen use efficiency. Leaf carbon isotope discrimination (delta(13)C) and leaf nitrogen isotopic composition (delta(15)N) were analyzed, but no significant QTLs were found for these traits. Interestingly, two neighboring loci explained a relatively large percentage of the variation in leaf nitrogen per unit area. This was notable because leaf nitrogen has been shown to strongly affect the fitness of early-generation sunflower hybrids in the H. anomalus habitat, and QTLs of large effect are expected to respond relatively quickly to selection. We speculate that the genetic architecture underlying leaf nitrogen may have facilitated the colonization of active desert sand dunes by H. anomalus.     

Association genetics of complex traits in plants

Association mapping is rapidly becoming the main method for dissecting the genetic architecture of complex traits in plants. Currently most association mapping studies in plants are preformed using sets of genes selected to be putative candidates for the trait of interest, but rapid developments in genomics will allow for genome-wide mapping in virtually any plant species in the near future. As the costs for genotyping are decreasing, the focus has shifted towards phenotyping. In plants, clonal replication and/or inbred lines allows for replicated phenotyping under many different environmental conditions. Reduced sequencing costs will increase the number of studies that use RNA sequencing data to perform expression quantitative trait locus (eQTL) mapping, which will increase our knowledge of how gene expression variation contributes to phenotypic variation. Current population sizes used in association mapping studies are modest in size and need to be greatly increased if mutations explaining less than a few per cent of the phenotypic variation are to be detected. Association mapping has started to yield insights into the genetic architecture of complex traits in plants, and future studies with greater genome coverage will help to elucidate how plants have managed to adapt to a wide variety of environmental conditions.     

Mapping the genetic architecture of complex traits in experimental populations

SUMMARY: Understanding how interactions among set of genes affect diverse phenotypes is having a greater impact on biomedical research, agriculture and evolutionary biology. Mapping and characterizing the isolated effects of single quantitative trait locus (QTL) is a first step, but we also need to assemble networks of QTLs and define non-additive interactions (epistasis) together with a host of potential environmental modulators. In this article, we present a full-QTL model with which to explore the genetic architecture of complex trait in multiple environments. Our model includes the effects of multiple QTLs, epistasis, QTL-by-environment interactions and epistasis-by-environment interactions. A new mapping strategy, including marker interval selection, detection of marker interval interactions and genome scans, is used to evaluate putative locations of multiple QTLs and their interactions. All the mapping procedures are performed in the framework of mixed linear model that are flexible to model environmental factors regardless of fix or random effects being assumed. An F-statistic based on Henderson method III is used for hypothesis tests. This method is less computationally greedy than corresponding likelihood ratio test. In each of the mapping procedures, permutation testing is exploited to control for genome-wide false positive rate, and model selection is used to reduce ghost peaks in F-statistic profile. Parameters of the full-QTL model are estimated using a Bayesian method via Gibbs sampling. Monte Carlo simulations help define the reliability and efficiency of the method. Two real-world phenotypes (BXD mouse olfactory bulb weight data and rice yield data) are used as exemplars to demonstrate our methods. AVAILABILITY: A software package is freely available at http://ibi.zju.edu.cn/software/qtlnetwork     

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.     

Assessing the complex architecture of polygenic traits in diverged yeast populations

Phenotypic variation arising from populations adapting to different niches has a complex underlying genetic architecture. A major challenge in modern biology is to identify the causative variants driving phenotypic variation. Recently, the baker's yeast, Saccharomyces cerevisiae has emerged as a powerful model for dissecting complex traits. However, past studies using a laboratory strain were unable to reveal the complete architecture of polygenic traits. Here, we present a linkage study using 576 recombinant strains obtained from crosses of isolates representative of the major lineages. The meiotic recombinational landscape appears largely conserved between populations; however, strain-specific hotspots were also detected. Quantitative measurements of growth in 23 distinct ecologically relevant environments show that our recombinant population recapitulates most of the standing phenotypic variation described in the species. Linkage analysis detected an average of 6.3 distinct QTLs for each condition tested in all crosses, explaining on average 39% of the phenotypic variation. The QTLs detected are not constrained to a small number of loci, and the majority are specific to a single cross-combination and to a specific environment. Moreover, crosses between strains of similar phenotypes generate greater variation in the offspring, suggesting the presence of many antagonistic alleles and epistatic interactions. We found that subtelomeric regions play a key role in defining individual quantitative variation, emphasizing the importance of the adaptive nature of these regions in natural populations. This set of recombinant strains is a powerful tool for investigating the complex architecture of polygenic traits.     

Genetic and environmental effects on complex traits in mice

The interaction between genotype and environment is recognized as an important source of experimental variation when complex traits are measured in the mouse, but the magnitude of that interaction has not often been measured. From a study of 2448 genetically heterogeneous mice, we report the heritability of 88 complex traits that include models of human disease (asthma, type 2 diabetes mellitus, obesity, and anxiety) as well as immunological, biochemical, and hematological phenotypes. We show that environmental and physiological covariates are involved in an unexpectedly large number of significant interactions with genetic background. The 15 covariates we examined have a significant effect on behavioral and physiological tests, although they rarely explain >10% of the variation. We found that interaction effects are more frequent and larger than the main effects: half of the interactions explained >20% of the variance and in nine cases exceeded 50%. Our results indicate that assays of gene function using mouse models should take into account interactions between gene and environment.     

Genetic architecture of traits associated with serpentine adaptation of Silene vulgaris

Serpentine soils provide a difficult substrate for plant colonization and growth and therefore represent an ideal system for studying the genetics of habitat adaptation and the evolution of plant-ecotypes. Using an F2 mapping population derived from an intraspecific cross between a serpentine and a nonserpentine ecotype of Silene vulgaris, the genetic architecture of seven morphological, physiological and life-history traits was explored. A quantitative trait locus (QTL) analysis identified 23 QTLs, 15 of which were classified as major QTLs. The observed genetic architecture suggests that traits potentially involved in habitat adaptation are controlled by few genes of major effect and have evolved under consistent directional selection. Several linkage groups harboured overlapping QTLs for different traits, which can be due to either pleiotropy or linkage. The potential roles of these factors and of the time available for habitat adaptation and ecological speciation on serpentine are discussed.     

Genetic architecture of complex traits and accuracy of genomic prediction: coat colour, milk-fat percentage, and type in Holstein cattle as contrasting model traits

Prediction of genetic merit using dense SNP genotypes can be used for estimation of breeding values for selection of livestock, crops, and forage species; for prediction of disease risk; and for forensics. The accuracy of these genomic predictions depends in part on the genetic architecture of the trait, in particular number of loci affecting the trait and distribution of their effects. Here we investigate the difference among three traits in distribution of effects and the consequences for the accuracy of genomic predictions. Proportion of black coat colour in Holstein cattle was used as one model complex trait. Three loci, KIT, MITF, and a locus on chromosome 8, together explain 24% of the variation of proportion of black. However, a surprisingly large number of loci of small effect are necessary to capture the remaining variation. A second trait, fat concentration in milk, had one locus of large effect and a host of loci with very small effects. Both these distributions of effects were in contrast to that for a third trait, an index of scores for a number of aspects of cow confirmation ("overall type"), which had only loci of small effect. The differences in distribution of effects among the three traits were quantified by estimating the distribution of variance explained by chromosome segments containing 50 SNPs. This approach was taken to account for the imperfect linkage disequilibrium between the SNPs and the QTL affecting the traits. We also show that the accuracy of predicting genetic values is higher for traits with a proportion of large effects (proportion black and fat percentage) than for a trait with no loci of large effect (overall type), provided the method of analysis takes advantage of the distribution of loci effects.     

Genetic dissection of complex traits using hierarchical biological knowledge

Despite the growing constellation of genetic loci linked to common traits, these loci have yet to account for most heritable variation, and most act through poorly understood mechanisms. Recent machine learning (ML) systems have used hierarchical biological knowledge to associate genetic mutations with phenotypic outcomes, yielding substantial predictive power and mechanistic insight. Here, we use an ontology-guided ML system to map single nucleotide variants (SNVs) focusing on 6 classic phenotypic traits in natural yeast populations. The 29 identified loci are largely novel and account for ~17% of the phenotypic variance, versus <3% for standard genetic analysis. Representative results show that sensitivity to hydroxyurea is linked to SNVs in two alternative purine biosynthesis pathways, and that sensitivity to copper arises through failure to detoxify reactive oxygen species in fatty acid metabolism. This work demonstrates a knowledge-based approach to amplifying and interpreting signals in population genetic studies.     

QTLNetwork: mapping and visualizing genetic architecture of complex traits in experimental populations

SUMMARY: QTLNetwork is a software package for mapping and visualizing the genetic architecture underlying complex traits for experimental populations derived from a cross between two inbred lines. It can simultaneously map quantitative trait loci (QTL) with individual effects, epistasis and QTL-environment interaction. Currently, it is able to handle data from F(2), backcross, recombinant inbred lines and double-haploid populations, as well as populations from specific mating designs (immortalized F(2) and BC(n)F(n) populations). The Windows version of QTLNetwork was developed with a graphical user interface. Alternatively, the command-line versions have the facility to be run in other prevalent operating systems, such as Linux, Unix and MacOS. AVAILABILITY: http://ibi.zju.edu.cn/software/qtlnetwork.     

Genetic architecture of aerial and root traits in field-grown grafted grapevines is largely independent

Key message

QTLs were identified for traits assessed on field-grown grafted grapevines. Root number and section had the largest phenotypic variance explained. Genetic control of root and aerial traits was independent.

Abstract

Breeding new rootstocks for perennial crops remains challenging, mainly because of the number of desirable traits which have to be combined, these traits include good rooting ability and root development. Consequently, the present study analyzes the genetic architecture of root traits in grapevine. A segregating progeny of 138 F1 genotypes issued from an inter-specific cross between Vitis vinifera cv. Cabernet-Sauvignon × V. riparia cv. Gloire de Montpellier, used as rootstock, was phenotyped in grafted plants grown for 2 years in the field. Seven traits, related to aerial and root development, were quantified. Heritability ranged between 0.44 for aerial biomass to 0.7 for root number. Total root number was related to the number of fine roots, while root biomass was related to the number of coarse roots. Significant quantitative trait loci (QTLs) were identified for all the traits studied with some of them explaining approximately 20% of phenotypic variance. Only a single QTL co-localized for root and aerial biomass. Identified QTLs for aerial-to-root biomass ratio suggest that aerial and root traits are controlled independently. Genes known to be involved in auxin signaling pathways and phosphorus nutrition, whose orthologues were previously shown to regulate root development in Arabidopsis, were located in the confidence intervals of several QTLs. This study opens new perspectives for breeding rootstocks with improved root development capacities.

     

Nuclear architecture in plants

Nuclei are dynamic structures that move through the mitotic cell cycle, are involved in differentiation, and divide and fuse during reproduction. The DNA contents of nuclei from different plants vary by 2500-fold. The design and structure of nuclei is, therefore, both flexible and versatile. Features relating to genome, chromosome, and maybe even gene localization during interphase are now emerging. At the chromosomal level, studies of scaffold associations and DNA sequence organization are indicating structures that impose nuclear architecture.     

Nuclear architecture in plants

Structure within the nucleus of plants is becoming increasingly clear in both metaphase and interphase nuclei, although there are conflicting data about the relative positions of individual and pairs of chromosomes. At interphase, individual chromosomes may generally occupy discrete domains that are not intermixed with other chromosomes. Aspects of mechanical chromosome behaviour and even of gene expression may correlate with interphase chromosome position, and imply that a better understanding of nuclear architecture is required.     

Sexually dimorphic genetic architecture of complex traits in a large-scale F2 cross in pigs

Background

It is common for humans and model organisms to exhibit sexual dimorphism in a variety of complex traits. However, this phenomenon has rarely been explored in pigs.

Results

To investigate the genetic contribution to sexual dimorphism in complex traits in pigs, we conducted a sex-stratified analysis on 213 traits measured in 921 individuals produced by a White Duroc × Erhualian F₂ cross. Of the 213 traits examined, 102 differed significantly between the two sexes (q value <0.05), which indicates that sex is an important factor that influences a broad range of traits in pigs. We compared the estimated heritability of these 213 traits between males and females. In particular, we found that traits related to meat quality and fatty acid composition were significantly different between the two sexes, which shows that genetic factors contribute to variation in sexual dimorphic traits. Next, we performed a genome-wide association study (GWAS) in males and females separately; this approach allowed us to identify 13.6% more significant trait-SNP (single nucleotide polymorphism) associations compared to the number of associations identified in a GWAS that included both males and females. By comparing the allelic effects of SNPs in the two sexes, we identified 43 significant sexually dimorphic SNPs that were associated with 22 traits; 41 of these 43 loci were autosomal. The most significant sexually dimorphic loci were found to be associated with muscle hue angle and Minolta a* values (which are parameters that reflect the redness of meat) and were located between 9.3 and 10.7 Mb on chromosome 6. A nearby gene i.e. NUDT7 that plays an important role in heme synthesis is a strong candidate gene.

Conclusions

This study illustrates that sex is an important factor that influences phenotypic values and modifies the effects of the genetic variants that underlie complex traits in pigs; it also emphasizes the importance of stratifying by sex when performing GWAS.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0076-2) contains supplementary material, which is available to authorized users.     

Genetic variation and covariation of aphid life-history traits across unrelated host plants

A central paradigm of life-history theory is the existence of resource mediated trade-offs among different traits that contribute to fitness, yet observations inconsistent with this tenet are not uncommon. We previously found a clonal population of the aphid Myzus persicae to exhibit positive genetic correlations among major components of fitness, resulting in strong heritable fitness differences on a common host. This raises the question of how this genetic variation is maintained. One hypothesis states that variation for resource acquisition on different hosts may override variation for allocation, predicting strong fitness differences within hosts as a rule, but changes in fitness hierarchies across hosts due to trade-offs. Therefore, we carried out a life-table experiment with 17 clones of M. persicae, reared on three unrelated host plants: radish, common lambsquarters and black nightshade. We estimated the broad-sense heritabilities of six life-history traits on each host, the genetic correlations among traits within hosts, and the genetic correlations among traits on different hosts (cross-environment genetic correlations). The three plants represented radically different environments with strong effects on performance of M. persicae, yet we detected little evidence for trade-offs. Fitness components were positively correlated within hosts but also between the two more benign hosts (radish and lambsquarters), as well as between those and another host tested earlier. The comparison with the most stressful host, nightshade, was hampered by low survival. Survival on nightshade also exhibited genetic variation but was unrelated to fitness on other hosts. Acknowledging that the number of environments was necessarily limited in a quantitative genetic experiment, we suggest that the rather consistent fitness hierarchies across very different plants provided little evidence to support the idea that the clonal variation for life-history traits and their covariance structure are maintained by strong genotypexenvironment interactions with respect to hosts. Alternative explanations are discussed.