Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example

Key message

A comprehensive linkage atlas for seed yield in rapeseed.

Abstract

Most agronomic traits of interest for crop improvement (including seed yield) are highly complex quantitative traits controlled by numerous genetic loci, which brings challenges for comprehensively capturing associated markers/genes. We propose that multiple trait interactions underlie complex traits such as seed yield, and that considering these component traits and their interactions can dissect individual quantitative trait loci (QTL) effects more effectively and improve yield predictions. Using a segregating rapeseed (Brassica napus) population, we analyzed a large set of trait data generated in 19 independent experiments to investigate correlations between seed yield and other complex traits, and further identified QTL in this population with a SNP-based genetic bin map. A total of 1904 consensus QTL accounting for 22 traits, including 80 QTL directly affecting seed yield, were anchored to the B. napus reference sequence. Through trait association analysis and QTL meta-analysis, we identified a total of 525 indivisible QTL that either directly or indirectly contributed to seed yield, of which 295 QTL were detected across multiple environments. A majority (81.5%) of the 525 QTL were pleiotropic. By considering associations between traits, we identified 25 yield-related QTL previously ignored due to contrasting genetic effects, as well as 31 QTL with minor complementary effects. Implementation of the 525 QTL in genomic prediction models improved seed yield prediction accuracy. Dissecting the genetic and phenotypic interrelationships underlying complex quantitative traits using this method will provide valuable insights for genomics-based crop improvement.

     

Genetic architecture of hybrid male sterility in Drosophila: analysis of intraspecies variation for interspecies isolation

Background

The genetic basis of postzygotic isolation is a central puzzle in evolutionary biology. Evolutionary forces causing hybrid sterility or inviability act on the responsible genes while they still are polymorphic, thus we have to study these traits as they arise, before isolation is complete.

Methodology/Principal Findings

Isofemale strains of D. mojavensis vary significantly in their production of sterile F₁ sons when females are crossed to D. arizonae males. We took advantage of the intraspecific polymorphism, in a novel design, to perform quantitative trait locus (QTL) mapping analyses directly on F₁ hybrid male sterility itself. We found that the genetic architecture of the polymorphism for hybrid male sterility (HMS) in the F₁ is complex, involving multiple QTL, epistasis, and cytoplasmic effects.

Conclusions/Significance

The role of extensive intraspecific polymorphism, multiple QTL, and epistatic interactions in HMS in this young species pair shows that HMS is arising as a complex trait in this system. Directional selection alone would be unlikely to maintain polymorphism at multiple loci, thus we hypothesize that directional selection is unlikely to be the only evolutionary force influencing postzygotic isolation.     

QTL affecting stress response to crowding in a rainbow trout broodstock population

Background

Genomic analyses have the potential to impact selective breeding programs by identifying markers that serve as proxies for traits which are expensive or difficult to measure. Also, identifying genes affecting traits of interest enhances our understanding of their underlying biochemical pathways. To this end we conducted genome scans of seven rainbow trout families from a single broodstock population to identify quantitative trait loci (QTL) having an effect on stress response to crowding as measured by plasma cortisol concentration. Our goal was to estimate the number of major genes having large effects on this trait in our broodstock population through the identification of QTL.

Results

A genome scan including 380 microsatellite markers representing 29 chromosomes resulted in the de novo construction of genetic maps which were in good agreement with the NCCCWA genetic map. Unique sets of QTL were detected for two traits which were defined after observing a low correlation between repeated measurements of plasma cortisol concentration in response to stress. A highly significant QTL was detected in three independent analyses on Omy16, many additional suggestive and significant QTL were also identified. With linkage-based methods of QTL analysis such as half-sib regression interval mapping and a variance component method, we determined that the significant and suggestive QTL explain about 40-43% and 13-27% of the phenotypic trait variation, respectively.

Conclusions

The cortisol response to crowding stress is a complex trait controlled in a sub-sample of our broodstock population by multiple QTL on at least 8 chromosomes. These QTL are largely different from others previously identified for a similar trait, documenting that population specific genetic variants independently affect cortisol response in ways that may result in different impacts on growth. Also, mapping QTL for multiple traits associated with stress response detected trait specific QTL which indicate the significance of the first plasma cortisol measurement in defining the trait. Fine mapping these QTL can lead towards the identification of genes affecting stress response and may influence approaches to selection for this economically important stress response trait.

     

Quantitative trait loci that control the oil content variation of rapeseed (Brassica napus L.)

Key message

This report describes an integrative analysis of seed-oil-content quantitative trait loci (QTL) in Brassica napus , using a high-density genetic map to align QTL among different populations.

Abstract

Rapeseed (Brassica napus) is an important source of edible oil and sustainable energy. Given the challenge involved in using only a few genes to substantially increase the oil content of rapeseed without affecting the fatty acid composition, exploitation of a greater number of genetic loci that regulate the oil content variation among rapeseed germplasm is of fundamental importance. In this study, we investigated variation in the seed-oil content among two related genetic populations of Brassica napus, the TN double-haploid population and its derivative reconstructed-F₂ population. Each population was grown in multiple experiments under different environmental conditions. Mapping of quantitative trait loci (QTL) identified 41 QTL in the TN populations. Furthermore, of the 20 pairs of epistatic interaction loci detected, approximately one-third were located within the QTL intervals. The use of common markers on different genetic maps and the TN genetic map as a reference enabled us to project QTL from an additional three genetic populations onto the TN genetic map. In summary, we used the TN genetic map of the B. napus genome to identify 46 distinct QTL regions that control seed-oil content on 16 of the 19 linkage groups of B. napus. Of these, 18 were each detected in multiple populations. The present results are of value for ongoing efforts to breed rapeseed with high oil content, and alignment of the QTL makes an important contribution to the development of an integrative system for genetic studies of rapeseed.     

Accounting for a quantitative trait locus for plasma triglyceride levels: utilization of variants in multiple genes

Background

For decades, research efforts have tried to uncover the underlying genetic basis of human susceptibility to a variety of diseases. Linkage studies have resulted in highly replicated findings and helped identify quantitative trait loci (QTL) for many complex traits; however identification of specific alleles accounting for linkage remains elusive. The purpose of this study was to determine whether with a sufficient number of variants a linkage signal can be fully explained.

Method

We used comprehensive fine-mapping using a dense set of single nucleotide polymorphisms (SNPs) across the entire quantitative trait locus (QTL) on human chromosome 7q36 linked to plasma triglyceride levels. Analyses included measured genotype and combined linkage association analyses.

Results

Screening this linkage region, we found an over representation of nominally significant associations in five genes (MLL3, DPP6, PAXIP1, HTR5A, INSIG1). However, no single genetic variant was sufficient to account for the linkage. On the other hand, multiple variants capturing the variation in these five genes did account for the linkage at this locus. Permutation analyses suggested that this reduction in LOD score was unlikely to have occurred by chance (p = 0.008).

Discussion

With recent findings, it has become clear that most complex traits are influenced by a large number of genetic variants each contributing only a small percentage to the overall phenotype. We found that with a sufficient number of variants, the linkage can be fully explained. The results from this analysis suggest that perhaps the failure to identify causal variants for linkage peaks may be due to multiple variants under the linkage peak with small individual effect, rather than a single variant of large effect.     

solQTL: a tool for QTL analysis,visualization and linking to genomes at SGN database

Background  

A common approach to understanding the genetic basis of complex traits is through identification of associated quantitative trait loci (QTL). Fine mapping QTLs requires several generations of backcrosses and analysis of large populations, which is time-consuming and costly effort. Furthermore, as entire genomes are being sequenced and an increasing amount of genetic and expression data are being generated, a challenge remains: linking phenotypic variation to the underlying genomic variation. To identify candidate genes and understand the molecular basis underlying the phenotypic variation of traits, bioinformatic approaches are needed to exploit information such as genetic map, expression and whole genome sequence data of organisms in biological databases.     

Multiple-line cross QTL mapping for biomass yield and plant height in triticale (× Triticosecale Wittmack)

Key message

QTL mapping in multiple families identifies trait-specific and pleiotropic QTL for biomass yield and plant height in triticale.

Abstract

Triticale shows a broad genetic variation for biomass yield which is of interest for a range of purposes, including bioenergy. Plant height is a major contributor to biomass yield and in this study, we investigated the genetic architecture underlying biomass yield and plant height by multiple-line cross QTL mapping. We employed 647 doubled haploid lines from four mapping populations that have been evaluated in four environments and genotyped with 1710 DArT markers. Twelve QTL were identified for plant height and nine for biomass yield which cross-validated explained 59.6 and 38.2 % of the genotypic variance, respectively. A major QTL for both traits was identified on chromosome 5R which likely corresponds to the dominant dwarfing gene Ddw1. In addition, we detected epistatic QTL for plant height and biomass yield which, however, contributed only little to the genetic architecture of the traits. In conclusion, our results demonstrate the potential of genomic approaches for a knowledge-based improvement of biomass yield in triticale.     

QTLminer: identifying genes regulating quantitative traits

Background  

Quantitative trait locus (QTL) mapping identifies genomic regions that likely contain genes regulating a quantitative trait. However, QTL regions may encompass tens to hundreds of genes. To find the most promising candidate genes that regulate the trait, the biologist typically collects information from multiple resources about the genes in the QTL interval. This process is very laborious and time consuming.     

QTL Mapping in New Arabidopsis thaliana Advanced Intercross-Recombinant Inbred Lines

Background

Even when phenotypic differences are large between natural or domesticated strains, the underlying genetic basis is often complex, and causal genomic regions need to be identified by quantitative trait locus (QTL) mapping. Unfortunately, QTL positions typically have large confidence intervals, which can, for example, lead to one QTL being masked by another, when two closely linked loci are detected as a single QTL. One strategy to increase the power of precisely localizing small effect QTL, is the use of an intercross approach before inbreeding to produce Advanced Intercross RILs (AI-RILs).

Methodology/Principal Findings

We present two new AI-RIL populations of Arabidopsis thaliana genotyped with an average intermarker distance of 600 kb. The advanced intercrossing design led to expansion of the genetic map in the two populations, which contain recombination events corresponding to 50 kb/cM in an F₂ population. We used the AI-RILs to map QTL for light response and flowering time, and to identify segregation distortion in one of the AI-RIL populations due to a negative epistatic interaction between two genomic regions.

Conclusions/Significance

The two new AI-RIL populations, EstC and KendC, derived from crosses of Columbia (Col) to Estland (Est-1) and Kendallville (Kend-L) provide an excellent resource for high precision QTL mapping. Moreover, because they have been genotyped with over 100 common markers, they are also excellent material for comparative QTL mapping.     

Joint QTL linkage mapping for multiple-cross mating design sharing one common parent

Background

Nested association mapping (NAM) is a novel genetic mating design that combines the advantages of linkage analysis and association mapping. This design provides opportunities to study the inheritance of complex traits, but also requires more advanced statistical methods. In this paper, we present the detailed algorithm of a QTL linkage mapping method suitable for genetic populations derived from NAM designs. This method is called joint inclusive composite interval mapping (JICIM). Simulations were designed on the detected QTL in a maize NAM population and an Arabidopsis NAM population so as to evaluate the efficiency of the NAM design and the JICIM method.

Principal Findings

Fifty-two QTL were identified in the maize population, explaining 89% of the phenotypic variance of days to silking, and nine QTL were identified in the Arabidopsis population, explaining 83% of the phenotypic variance of flowering time. Simulations indicated that the detection power of these identified QTL was consistently high, especially for large-effect QTL. For rare QTL having significant effects in only one family, the power of correct detection within the 5 cM support interval was around 80% for 1-day effect QTL in the maize population, and for 3-day effect QTL in the Arabidopsis population. For smaller-effect QTL, the power diminished, e.g., it was around 50% for maize QTL with an effect of 0.5 day. When QTL were linked at a distance of 5 cM, the likelihood of mapping them as two distinct QTL was about 70% in the maize population. When the linkage distance was 1 cM, they were more likely mapped as one single QTL at an intermediary position.

Conclusions

Because it takes advantage of the large genetic variation among parental lines and the large population size, NAM is a powerful multiple-cross design for complex trait dissection. JICIM is an efficient and specialty method for the joint QTL linkage mapping of genetic populations derived from the NAM design.     

Bayesian QTL analyses using pedigreed families of an outcrossing species,with application to fruit firmness in apple

Key message

Proof of concept of Bayesian integrated QTL analyses across pedigree-related families from breeding programs of an outbreeding species. Results include QTL confidence intervals, individuals’ genotype probabilities and genomic breeding values.

Abstract

Bayesian QTL linkage mapping approaches offer the flexibility to study multiple full sib families with known pedigrees simultaneously. Such a joint analysis increases the probability of detecting these quantitative trait loci (QTL) and provide insight of the magnitude of QTL across different genetic backgrounds. Here, we present an improved Bayesian multi-QTL pedigree-based approach on an outcrossing species using progenies with different (complex) genetic relationships. Different modeling assumptions were studied in the QTL analyses, i.e., the a priori expected number of QTL varied and polygenic effects were considered. The inferences include number of QTL, additive QTL effect sizes and supporting credible intervals, posterior probabilities of QTL genotypes for all individuals in the dataset, and QTL-based as well as genome-wide breeding values. All these features have been implemented in the FlexQTL^? software. We analyzed fruit firmness in a large apple dataset that comprised 1,347 individuals forming 27 full sib families and their known ancestral pedigrees, with genotypes for 87 SSR markers on 17 chromosomes. We report strong or positive evidence for 14 QTL for fruit firmness on eight chromosomes, validating our approach as several of these QTL were reported previously, though dispersed over a series of studies based on single mapping populations. Interpretation of linked QTL was possible via individuals’ QTL genotypes. The correlation between the genomic breeding values and phenotypes was on average 90 %, but varied with the number of detected QTL in a family. The detailed posterior knowledge on QTL of potential parents is critical for the efficiency of marker-assisted breeding.     

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

Key message

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.

Abstract

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.     

Pheno2Geno - High-throughput generation of genetic markers and maps from molecular phenotypes for crosses between inbred strains

Background

Genetic markers and maps are instrumental in quantitative trait locus (QTL) mapping in segregating populations. The resolution of QTL localization depends on the number of informative recombinations in the population and how well they are tagged by markers. Larger populations and denser marker maps are better for detecting and locating QTLs. Marker maps that are initially too sparse can be saturated or derived de novo from high-throughput omics data, (e.g. gene expression, protein or metabolite abundance). If these molecular phenotypes are affected by genetic variation due to a major QTL they will show a clear multimodal distribution. Using this information, phenotypes can be converted into genetic markers.

Results

The Pheno2Geno tool uses mixture modeling to select phenotypes and transform them into genetic markers suitable for construction and/or saturation of a genetic map. Pheno2Geno excludes candidate genetic markers that show evidence for multiple possibly epistatically interacting QTL and/or interaction with the environment, in order to provide a set of robust markers for follow-up QTL mapping.We demonstrate the use of Pheno2Geno on gene expression data of 370,000 probes in 148 A. thaliana recombinant inbred lines. Pheno2Geno is able to saturate the existing genetic map, decreasing the average distance between markers from 7.1 cM to 0.89 cM, close to the theoretical limit of 0.68 cM (with 148 individuals we expect a recombination every 100/148=0.68 cM); this pinpointed almost all of the informative recombinations in the population.

Conclusion

The Pheno2Geno package makes use of genome-wide molecular profiling and provides a tool for high-throughput de novo map construction and saturation of existing genetic maps. Processing of the showcase dataset takes less than 30 minutes on an average desktop PC. Pheno2Geno improves QTL mapping results at no additional laboratory cost and with minimum computational effort. Its results are formatted for direct use in R/qtl, the leading R package for QTL studies. Pheno2Geno is freely available on CRAN under “GNU GPL v3”. The Pheno2Geno package as well as the tutorial can also be found at: http://pheno2geno.nl.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0475-6) contains supplementary material, which is available to authorized users.     

Susceptibility to klebsiella pneumonaie infection in collaborative cross mice is a complex trait controlled by at least three loci acting at different time points

Background

Klebsiella pneumoniae (Kp) is a bacterium causing severe pneumonia in immunocompromised hosts and is often associated with sepsis. With the rise of antibiotic resistant bacteria, there is a need for new effective and affordable control methods; understanding the genetic architecture of susceptibility to Kp will help in their development. We performed the first quantitative trait locus (QTL) mapping study of host susceptibility to Kp infection in immunocompetent Collaborative Cross mice (CC). We challenged 328 mice from 73 CC lines intraperitoneally with 10⁴ colony forming units of Kp strain K2. Survival and body weight were monitored for 15 days post challenge. 48 of the CC lines were genotyped with 170,000 SNPs, with which we mapped QTLs.

Results

CC lines differed significantly (P < 0.05) in mean survival time, between 1 to 15 days post infection, and broad sense heritability was 0.45. Distinct QTL were mapped at specific time points during the challenge. A QTL on chromosome 4 was found only on day 2 post infection, and QTL on chromosomes 8 and 18, only on day 8. By using the sequence variations of the eight inbred strain founders of the CC to refine QTL localization we identify several candidate genes.

Conclusion

Host susceptibility to Kp is a complex trait, controlled by multiple genetic factors that act sequentially during the course of infection.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-865) contains supplementary material, which is available to authorized users.     

Functional dissection of the Drosophila Kallmann's syndrome protein DmKal-1

Background

The proportion of muscle fibre types and their size affect muscularity as well as functional properties of the musculature and meat quality. We aimed to identify QTL for microstructural muscle properties including muscle fibre size, their numbers and fibre type proportions as well as biophysical parameters of meat quality and traits related to body composition, i.e. pH, conductivity, area of M. longissimus dorsi and lean meat content. A QTL scan was conducted in a porcine experimental population that is based on Duroc and Berlin Miniature Pig.

Results

Least square regression interval mapping revealed five significant and 42 suggestive QTL for traits related to muscle fibre composition under the line-cross model as well as eight significant and 40 suggestive QTL under the half-sib model. For traits related to body composition and biophysical parameters of meat quality five and twelve significant plus nine and 22 suggestive QTL were found under the line-cross and half-sib model, respectively. Regions with either significant QTL for muscle fibre traits or significant QTL for meat quality and muscularity or both were detected on SSC1, 2, 3, 4, 5, 13, 14, 15, and 16. QTL for microstructural properties explained a larger proportion of variance than did QTL for meat quality and body composition.

Conclusion

Microstructural properties of pig muscle and meat quality are governed by genetic variation at many loci distributed throughout the genome. QTL analysis under both, the line-cross and half-sib model, allows detecting QTL in case of fixation or segregation of the QTL alleles among the founder populations and thus provide comprehensive insight into the genetic variation of the traits under investigation. Genomic regions affecting complex traits of muscularity and meat quality as well as microstructural properties might point to QTL that in first instance affect muscle fibre traits and by this in second instance meat quality. Disentangling complex traits in their constituent phenotypes might facilitate the identification of QTL and the elucidation of the pleiotropic nature of QTL effects.     

Identification of a <Emphasis Type="Italic">Rice stripe necrosis virus</Emphasis> resistance locus and yield component QTLs using <Emphasis Type="Italic">Oryza sativa</Emphasis> × <Emphasis Type="Italic">O. glaberrima</Emphasis> introgression lines

Background  

Developing new population types based on interspecific introgressions has been suggested by several authors to facilitate the discovery of novel allelic sources for traits of agronomic importance. Chromosome segment substitution lines from interspecific crosses represent a powerful and useful genetic resource for QTL detection and breeding programs.     

Genetic dynamics underlying phenotypic development of biomass yield in triticale

Background

The nature of dynamic traits with their phenotypic plasticity suggests that they are under the control of a dynamic genetic regulation. We employed a precision phenotyping platform to non-invasively assess biomass yield in a large mapping population of triticale at three developmental stages.

Results

Using multiple-line cross QTL mapping we identified QTL for each of these developmental stages which explained a considerable proportion of the genotypic variance. Some QTL were identified at each developmental stage and thus contribute to biomass yield throughout the studied developmental phases. Interestingly, we also observed QTL that were only identified for one or two of the developmental stages illustrating a temporal contribution of these QTL to the trait. In addition, epistatic QTL were detected and the epistatic interaction landscape was shown to dynamically change with developmental progression.

Conclusions

In summary, our results reveal the temporal dynamics of the genetic architecture underlying biomass accumulation in triticale and emphasize the need for a temporal assessment of dynamic traits.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-458) contains supplementary material, which is available to authorized users.     

Multi-environment QTL analysis of plant and flower morphological traits in tetraploid rose

Key message

Rose morphological traits such as prickles or petal number are influenced by a few key QTL which were detected across different growing environments—necessary for genomics-assisted selection in non-target environments.

Abstract

Rose, one of the world’s most-loved and commercially important ornamental plants, is predominantly tetraploid, possessing four rather than two copies of each chromosome. This condition complicates genetic analysis, and so the majority of previous genetic studies in rose have been performed at the diploid level. However, there may be advantages to performing genetic analyses at the tetraploid level, not least because this is the ploidy level of most breeding germplasm. Here, we apply recently developed methods for quantitative trait loci (QTL) detection in a segregating tetraploid rose population (F₁?=?151) to unravel the genetic control of a number of key morphological traits. These traits were measured both in the Netherlands and Kenya. Since ornamental plant breeding and selection are increasingly being performed at locations other than the production sites, environment-neutral QTL are required to maximise the effectiveness of breeding programmes. We detected a number of robust, multi-environment QTL for such traits as stem and petiole prickles, petal number and stem length that were localised on the recently developed high-density SNP linkage map for rose. Our work explores the complex genetic architecture of these important morphological traits at the tetraploid level, while helping to advance the methods for marker–trait exploration in polyploid species.