An Arabidopsis example of association mapping in structured samples

Standing genetic variation in FRIGIDA mediates experimental evolution of flowering time in Arabidopsis

The role of standing genetic variation in adaptive evolution remains unclear. Although there has been much progress in identifying candidate genes that underlie adaptive traits, we still lack direct evidence that natural allelic variation in these genes can actually mediate adaptive evolution. In this study, we investigate the role of natural allelic variation in two candidate flowering time genes, in response to selection for early flowering in Arabidopsis thaliana: FRIGIDA (FRI) and FLOWERING LOCUS C (FLC). We performed artificial selection for early flowering under 'spring-' and 'winter-annual' growth conditions using an outbred population of A. thaliana produced by intermating 19 natural accessions. FRI and FLC are involved in A. thaliana's response to winter conditions, and nonfunctional and weak alleles at these loci are know to reduce flowering time, particularly under spring-annual conditions. Our results provide direct evidence that natural allelic variation in FRI can provide rapid and predictable adaptive evolution in flowering time under spring-annual conditions. We observed a strong response to selection, in terms of reducing flowering time, in both growth conditions (approximately 2 standard deviation reduction). Concomitantly, the frequency of functional FRI alleles under spring-annual conditions was reduced by 68%, in agreement with predicted changes. No significant changes in allele frequencies were observed in FRI in the winter-annual growth condition or in FLC for either growth conditions. These results indicate that changes in flowering time are mediated by different genetic factors under spring- and winter-annual growth conditions, and that other loci must also be contributing to the response to selection.

Bayesian multilocus association mapping on ordinal and censored traits and its application to the analysis of genetic variation among Oryza sativa L. germplasms

Association mapping can be a powerful tool for detecting quantitative trait loci (QTLs) without requiring line-crossing experiments. We previously proposed a Bayesian approach for simultaneously mapping multiple QTLs by a regression method that directly incorporates estimates of the population structure. In the present study, we extended our method to analyze ordinal and censored traits, since both types of traits are common in the evaluation of germplasm collections. Ordinal-probit and tobit models were employed to analyze ordinal and censored traits, respectively. In both models, we postulated the existence of a latent continuous variable associated with the observable data, and we used a Markov-chain Monte Carlo algorithm to sample the latent variable and determine the model parameters. We evaluated the efficiency of our approach by using simulated- and real-trait analyses of a rice germplasm collection. Simulation analyses based on real marker data showed that our models could reduce both false-positive and false-negative rates in detecting QTLs to reasonable levels. Simulation analyses based on highly polymorphic marker data, which were generated by coalescent simulations, showed that our models could be applied to genotype data based on highly polymorphic marker systems, like simple sequence repeats. For the real traits, we analyzed heading date as a censored trait and amylose content and the shape of milled rice grains as ordinal traits. We found significant markers that may be linked to previously reported QTLs. Our approach will be useful for whole-genome association mapping of ordinal and censored traits in rice germplasm collections.

Comparison of mixed-model approaches for association mapping in rapeseed, potato, sugar beet, maize, and Arabidopsis

Background

In recent years, several attempts have been made in plant genetics to detect QTL by using association mapping methods. The objectives of this study were to (i) evaluate various methods for association mapping in five plant species and (ii) for three traits in each of the plant species compare the T_opt, the restricted maximum likelihood (REML) estimate of the conditional probability that two genotypes carry at the same locus alleles that are identical in state but not identical by descent. In order to compare the association mapping methods based on scenarios with realistic estimates of population structure and familial relatedness, we analyzed phenotypic and genotypic data of rapeseed, potato, sugar beet, maize, and Arabidopsis. For the same reason, QTL effects were simulated on top of the observed phenotypic values when examining the adjusted power for QTL detection.

Results

The correlation between the T_opt values identified using REML deviance profiles and profiles of the mean of squared difference between observed and expected P values was 0.83.

Conclusion

The mixed-model association mapping approaches using a kinship matrix, which was based on T_opt, were more appropriate for association mapping than the recently proposed QK method with respect to the adherence to the nominal α level and the adjusted power for QTL detection. Furthermore, we showed that T_opt differs considerably among the five plant species but only marginally among different traits.

Comparison of mixed-model approaches for association mapping in rapeseed, potato, sugar beet, maize, and Arabidopsis

BACKGROUND

In recent years, several attempts have been made in plant genetics to detect QTL by using association mapping methods. The objectives of this study were to (i) evaluate various methods for association mapping in five plant species and (ii) for three traits in each of the plant species compare the Topt, the restricted maximum likelihood (REML) estimate of the conditional probability that two genotypes carry at the same locus alleles that are identical in state but not identical by descent. In order to compare the association mapping methods based on scenarios with realistic estimates of population structure and familial relatedness, we analyzed phenotypic and genotypic data of rapeseed, potato, sugar beet, maize, and Arabidopsis. For the same reason, QTL effects were simulated on top of the observed phenotypic values when examining the adjusted power for QTL detection.

RESULTS

The correlation between the Topt values identified using REML deviance profiles and profiles of the mean of squared difference between observed and expected P values was 0.83.

CONCLUSION

The mixed-model association mapping approaches using a kinship matrix, which was based on Topt, were more appropriate for association mapping than the recently proposed QK method with respect to the adherence to the nominal alpha level and the adjusted power for QTL detection. Furthermore, we showed that Topt differs considerably among the five plant species but only marginally among different traits.

Bayesian multilocus association mapping on ordinal and censored traits and its application to the analysis of genetic variation among Oryza sativa L. germplasms

Association mapping can be a powerful tool for detecting quantitative trait loci (QTLs) without requiring line-crossing experiments. We previously proposed a Bayesian approach for simultaneously mapping multiple QTLs by a regression method that directly incorporates estimates of the population structure. In the present study, we extended our method to analyze ordinal and censored traits, since both types of traits are common in the evaluation of germplasm collections. Ordinal-probit and tobit models were employed to analyze ordinal and censored traits, respectively. In both models, we postulated the existence of a latent continuous variable associated with the observable data, and we used a Markov-chain Monte Carlo algorithm to sample the latent variable and determine the model parameters. We evaluated the efficiency of our approach by using simulated- and real-trait analyses of a rice germplasm collection. Simulation analyses based on real marker data showed that our models could reduce both false-positive and false-negative rates in detecting QTLs to reasonable levels. Simulation analyses based on highly polymorphic marker data, which were generated by coalescent simulations, showed that our models could be applied to genotype data based on highly polymorphic marker systems, like simple sequence repeats. For the real traits, we analyzed heading date as a censored trait and amylose content and the shape of milled rice grains as ordinal traits. We found significant markers that may be linked to previously reported QTLs. Our approach will be useful for whole-genome association mapping of ordinal and censored traits in rice germplasm collections.

Quantitative genomics: analyzing intraspecific variation using global gene expression polymorphisms or eQTLs

Scientific inquiries in fields ranging from ecology to plant breeding assess phenotypic variation within a plant species either to explain its presence or utilize its consequences. Frequently this natural genetic variation is studied via mapping quantitative trait loci (QTLs); however, elucidation of the underlying molecular mechanisms is a continuing bottleneck. The genomic analysis of transcripts as individual phenotypes has led to the emerging field of expression QTL analysis. This field has begun both to delve into the ecological/evolutionary significance of this transcript variation as well as to use specific eQTLs to speed up our analysis of the molecular basis of quantitative traits. This review introduces eQTL analysis and begins to illustrate how these data can be applied to multiple research fields.

Haplotype association mapping in mice

Haplotype Association Mapping (HAM) is a novel phenotype-driven approach to identify genetic loci and was originally developed for mice. This method, which is similar to Genome-Wide Association (GWA) studies in humans, looks for associations between the phenotype and the haplotypes of mouse inbred strains, treating inbred strains as individuals. Although this approach is still in development, we review the current literature, present the different methods and applications that are in use, and provide a glimpse of what is to come in the near future.

Molecular diversity and association mapping of fiber quality traits in exotic G. hirsutum L. germplasm

The narrow genetic base of cultivated cotton germplasm is hindering the cotton productivity worldwide. Although potential genetic diversity exists in Gossypium genus, it is largely 'underutilized' due to photoperiodism and the lack of innovative tools to overcome such challenges. The application of linkage disequilibrium (LD)-based association mapping is an alternative powerful molecular tool to dissect and exploit the natural genetic diversity conserved within cotton germplasm collections, greatly accelerating still 'lagging' cotton marker-assisted selection (MAS) programs. However, the extent of genome-wide linkage disequilibrium (LD) has not been determined in cotton. We report the extent of genome-wide LD and association mapping of fiber quality traits by using a 95 core set of microsatellite markers in a total of 285 exotic Gossypium hirsutum accessions, comprising of 208 landrace stocks and 77 photoperiodic variety accessions. We demonstrated the existence of useful genetic diversity within exotic cotton germplasm. In this germplasm set, 11-12% of SSR loci pairs revealed a significant LD. At the significance threshold (r(2)>/=0.1), a genome-wide average of LD declines within the genetic distance at <10 cM in the landrace stocks germplasm and >30 cM in variety germplasm. Genome wide LD at r(2)>/=0.2 was reduced on average to approximately 1-2 cM in the landrace stock germplasm and 6-8 cM in variety germplasm, providing evidence of the potential for association mapping of agronomically important traits in cotton. We observed significant population structure and relatedness in assayed germplasm. Consequently, the application of the mixed liner model (MLM), considering both kinship (K) and population structure (Q) detected between 6% and 13% of SSR markers associated with the main fiber quality traits in cotton. Our results highlight for the first time the feasibility and potential of association mapping, with consideration of the population structure and stratification existing in cotton germplasm resources. The number of SSR markers associated with fiber quality traits in diverse cotton germplasm, which broadly covered many historical meiotic events, should be useful to effectively exploit potentially new genetic variation by using MAS programs.

Genetic diversity among oat varieties of worldwide origin and associations of AFLP markers with quantitative traits

One hundred and fourteen oat (Avena sativa L.) varieties of worldwide origin were evaluated for genetic diversity based on 77 molecular polymorphisms produced by eight selective AFLP primer combinations. Genetic similarity, calculated using the DICE coefficient, was used for cluster analysis and principal component analysis was applied. In addition population structure was explored to identify discrete subpopulations based on allele frequency. Although clustering and population structure showed relationships with region and country of origin, there was no obvious relationship to hull presence or hull colour. Oat varieties originating from European breeding programs showed less diversity than varieties originating from North and South America. Associations between AFLP markers and agronomic traits (grain yield, groat yield, panicle emergence, plant height, and lodging) as well as kernel quality traits (kernel weight, test weight, screening percent and groat percent) were also investigated. Marker-trait associations were tested using a naïve simple regression model and five additional models that account for population structure. Significant associations were found for 23 AFLP markers, with many of these affecting multiple traits. This study demonstrates that diversity can be significantly enhanced using a global collection, and provides evidence for marker-trait associations that can be validated in segregating populations and exploited through marker-assisted selection.

Patterns of molecular evolution associated with two selective sweeps in the Tb1-Dwarf8 region in maize

We focused on a region encompassing a major maize domestication locus, Tb1, and a locus involved in the flowering time variation, Dwarf8 (D8), to investigate the consequences of two closely linked selective sweeps on nucleotide variation and gain some insights into maize geographical diffusion, through climate adaptation. First, we physically mapped D8 at approximately 300 kb 3' of Tb1. Second, we analyzed patterns of nucleotide variation at Tb1, D8, and seven short regions (400-700 bp) located in the Tb1-D8 region sequenced on a 40 maize inbred lines panel encompassing early-flowering temperate and late-flowering tropical lines. The pattern of polymorphism along the region is characterized by two valleys of depleted polymorphism while the region in between exhibits an appreciable amount of diversity. Our results reveal that a region approximately 100 kb upstream of the D8 gene exhibits hallmarks of divergent selection between temperate and tropical lines and is likely closer than the D8 gene to the target of selection for climate adaptation. Selection in the tropical lines appears more recent than in the temperate lines, suggesting an initial domestication of early-flowering maize. Simulation results indicate that the polymorphism pattern is consistent with two interfering selective sweeps at Tb1 and D8.

The genetic architecture of complex traits in teosinte (Zea mays ssp. parviglumis): new evidence from association mapping

Previous association analyses showed that variation at major regulatory genes contributes to standing variation for complex traits in Balsas teosinte, the progenitor of maize. This study expands our previous association mapping effort in teosinte by testing 123 markers in 52 candidate genes for association with 31 traits in a population of 817 individuals. Thirty-three significant associations for markers from 15 candidate genes and 10 traits survive correction for multiple testing. Our analyses suggest several new putative causative relationships between specific genes and trait variation in teosinte. For example, two ramosa genes (ra1 and ra2) associate with ear structure, and the MADS-box gene, zagl1, associates with ear shattering. Since zagl1 was previously shown to be a target of selection during maize domestication, we suggest that this gene was under selection for its effect on the loss of ear shattering, a key domestication trait. All observed effects were relatively small in terms of the percentage of phenotypic variation explained (<10%). We also detected several epistatic interactions between markers in the same gene that associate with the same trait. Candidate-gene-based association mapping appears to be a promising method for investigating the inheritance of complex traits in teosinte.

Accurate discovery of expression quantitative trait loci under confounding from spurious and genuine regulatory hotspots

In genomewide mapping of expression quantitative trait loci (eQTL), it is widely believed that thousands of genes are trans-regulated by a small number of genomic regions called "regulatory hotspots," resulting in "trans-regulatory bands" in an eQTL map. As several recent studies have demonstrated, technical confounding factors such as batch effects can complicate eQTL analysis by causing many spurious associations including spurious regulatory hotspots. Yet little is understood about how these technical confounding factors affect eQTL analyses and how to correct for these factors. Our analysis of data sets with biological replicates suggests that it is this intersample correlation structure inherent in expression data that leads to spurious associations between genetic loci and a large number of transcripts inducing spurious regulatory hotspots. We propose a statistical method that corrects for the spurious associations caused by complex intersample correlation of expression measurements in eQTL mapping. Applying our intersample correlation emended (ICE) eQTL mapping method to mouse, yeast, and human identifies many more cis associations while eliminating most of the spurious trans associations. The concordances of cis and trans associations have consistently increased between different replicates, tissues, and populations, demonstrating the higher accuracy of our method to identify real genetic effects.

Efficient mapping of plant height quantitative trait loci in a sorghum association population with introgressed dwarfing genes

Of the four major dwarfing genes described in sorghum, only Dw3 has been cloned. We used association mapping to characterize the phenotypic effects of the dw3 mutation and to fine map a second, epistatic dwarfing QTL on sorghum chromosome 9 (Sb-HT9.1). Our panel of 378 sorghum inbreds includes 230 sorghum conversion (SC) lines, which are exotic lines that have been introgressed with dwarfing quantitative trait loci (QTL) from a common parent. The causal mutation in dw3 associates with reduced lower internode length and an elongation of the apex, consistent with its role as an auxin efflux carrier. Lines carrying the dw3 mutation display high haplotype homozygosity over several megabases in the Dw3 region, but most markers linked to Dw3 do not associate significantly with plant height due to allele sharing between Dw3 and dw3 individuals. Using markers with a high mutation rate and the dw3 mutation as an interaction term, significant trait associations were detected across a 7-Mb region around Sb-HT9.1, largely due to higher detection power in the SC lines. Conversely, the likely QTL interval for Sb-HT9.1 was reduced to approximately 100 kb, demonstrating that the unique structure of this association panel provides both power and resolution for a genomewide scan.

Discordant longitudinal clines in flowering time and phytochrome C in Arabidopsis thaliana

Using seasonal cues to time reproduction appropriately is crucial for many organisms. Plants in particular often use photoperiod to signal the time to transition to flowering. Because seasonality varies latitudinally, adaptation to local climate is expected to result in corresponding clines in photoperiod-related traits. By experimentally manipulating photoperiod cues and measuring the flowering responses and photoperiod plasticity of 138 Eurasian accessions of Arabidopsis thaliana, we detected strong longitudinal but not latitudinal clines in flowering responses. The presence of longitudinal clines suggests that critical photoperiod cues vary among populations occurring at similar latitudes. Haplotypes at PHYC, a locus hypothesized to play a role in adaptation to light cues, were also longitudinally differentiated. Controlling for neutral population structure revealed that PHYC haplotype influenced flowering time; however, the distribution of PHYC haplotypes occurred in the opposite direction to the phenotypic cline, suggesting that loci other than PHYC are responsible for the longitudinal pattern in photoperiod response. Our results provide previously missing empirical support for the importance of PHYC in mediating photoperiod sensitivity in natural populations of A. thaliana. However, they also suggest that other loci and epistatic interactions likely play a role in the determination of flowering time and that the environmental factors influencing photoperiod in plants vary longitudinally as well as latitudinally.

Natural genetic variation of Arabidopsis thaliana is geographically structured in the Iberian peninsula

To understand the demographic history of Arabidopsis thaliana within its native geographical range, we have studied its genetic structure in the Iberian Peninsula region. We have analyzed the amount and spatial distribution of A. thaliana genetic variation by genotyping 268 individuals sampled in 100 natural populations from the Iberian Peninsula. Analyses of 175 individuals from 7 of these populations, with 20 chloroplast and nuclear microsatellite loci and 109 common single nucleotide polymorphisms, show significant population differentiation and isolation by distance. In addition, analyses of one genotype from 100 populations detected significant isolation by distance over the entire Iberian Peninsula, as well as among six Iberian subregions. Analyses of these 100 genotypes with different model-based clustering algorithms inferred four genetic clusters, which show a clear-cut geographical differentiation pattern. On the other hand, clustering analysis of a worldwide sample showed a west-east Eurasian longitudinal spatial gradient of the commonest Iberian genetic cluster. These results indicate that A. thaliana genetic variation displays significant regional structure and consistently support the hypothesis that Iberia has been a glacial refugium for A. thaliana. Furthermore, the Iberian geographical structure indicates a complex regional population dynamics, suggesting that this region contained multiple Pleistocene refugia with a different contribution to the postglacial colonization of Europe.

High-resolution mapping of gene expression using association in an outbred mouse stock

Quantitative trait locus (QTL) analysis is a powerful tool for mapping genes for complex traits in mice, but its utility is limited by poor resolution. A promising mapping approach is association analysis in outbred stocks or different inbred strains. As a proof of concept for the association approach, we applied whole-genome association analysis to hepatic gene expression traits in an outbred mouse population, the MF1 stock, and replicated expression QTL (eQTL) identified in previous studies of F2 intercross mice. We found that the mapping resolution of these eQTL was significantly greater in the outbred population. Through an example, we also showed how this precise mapping can be used to resolve previously identified loci (in intercross studies), which affect many different transcript levels (known as eQTL “hotspots”), into distinct regions. Our results also highlight the importance of correcting for population structure in whole-genome association studies in the outbred stock.

In rodents, as in humans, traits such as obesity or diabetes are under the influence of many genes spread throughout the genome. Using linkage analysis, the locations of the major contributing genes can be mapped only to very large regions of chromosomes, usually encompassing hundreds of genes. This has made it difficult to identify the underlying genes and mutations. Another approach, analogous to genome-wide association in human populations, is to use association analyses among outbred stocks of mice. In this proof-of-principle article, we make use of common variations that locally perturb gene expression to demonstrate the greatly improved mapping resolution of association in mice. Our results indicate that association analyses in mice are a powerful approach to the dissection of complex traits and their underlying molecular networks.

Evaluating statistical methods using plasmode data sets in the age of massive public databases: an illustration using false discovery rates

Plasmode is a term coined several years ago to describe data sets that are derived from real data but for which some truth is known. Omic techniques, most especially microarray and genomewide association studies, have catalyzed a new zeitgeist of data sharing that is making data and data sets publicly available on an unprecedented scale. Coupling such data resources with a science of plasmode use would allow statistical methodologists to vet proposed techniques empirically (as opposed to only theoretically) and with data that are by definition realistic and representative. We illustrate the technique of empirical statistics by consideration of a common task when analyzing high dimensional data: the simultaneous testing of hundreds or thousands of hypotheses to determine which, if any, show statistical significance warranting follow-on research. The now-common practice of multiple testing in high dimensional experiment (HDE) settings has generated new methods for detecting statistically significant results. Although such methods have heretofore been subject to comparative performance analysis using simulated data, simulating data that realistically reflect data from an actual HDE remains a challenge. We describe a simulation procedure using actual data from an HDE where some truth regarding parameters of interest is known. We use the procedure to compare estimates for the proportion of true null hypotheses, the false discovery rate (FDR), and a local version of FDR obtained from 15 different statistical methods.

Plasmode is a term used to describe a data set that has been derived from real data but for which some truth is known. Statistical methods that analyze data from high dimensional experiments (HDEs) seek to estimate quantities that are of interest to scientists, such as mean differences in gene expression levels and false discovery rates. The ability of statistical methods to accurately estimate these quantities depends on theoretical derivations or computer simulations. In computer simulations, data for which the true value of a quantity is known are often simulated from statistical models, and the ability of a statistical method to estimate this quantity is evaluated on the simulated data. However, in HDEs there are many possible statistical models to use, and which models appropriately produce data that reflect properties of real data is an open question. We propose the use of plasmodes as one answer to this question. If done carefully, plasmodes can produce data that reflect reality while maintaining the benefits of simulated data. We show one method of generating plasmodes and illustrate their use by comparing the performance of 15 statistical methods for estimating the false discovery rate in data from an HDE.

Demographic history of european populations of Arabidopsis thaliana

The model plant species Arabidopsis thaliana is successful at colonizing land that has recently undergone human-mediated disturbance. To investigate the prehistoric spread of A. thaliana, we applied approximate Bayesian computation and explicit spatial modeling to 76 European accessions sequenced at 876 nuclear loci. We find evidence that a major migration wave occurred from east to west, affecting most of the sampled individuals. The longitudinal gradient appears to result from the plant having spread in Europe from the east approximately 10,000 years ago, with a rate of westward spread of approximately 0.9 km/year. This wave-of-advance model is consistent with a natural colonization from an eastern glacial refugium that overwhelmed ancient western lineages. However, the speed and time frame of the model also suggest that the migration of A. thaliana into Europe may have accompanied the spread of agriculture during the Neolithic transition.

Association genetics in Pinus taeda L. II. Carbon isotope discrimination

Dissection of complex traits that influence fitness is not only a central topic in evolutionary research but can also assist breeding practices for economically important plant species, such as loblolly pine (Pinus taeda L). In this study, 46 single nucleotide polymorphisms (SNPs) from 41 disease and abiotic stress-inducible genes were tested for their genetic association with carbon isotope discrimination (CID), a time-integrated trait measure of stomatal conductance. A family-based approach to detect genotype/phenotype genetic association was developed for the first time in plants by applying the quantitative transmission disequilibrium test on an association population of 961 clones from 61 families (adopted from previous breeding programs) evaluated for phenotypic expression of CID at two sites. Two particularly promising candidates for their genetic effects on CID are: dhn-1, involved in stabilization of cell structures, and lp5-like, a glycine rich protein putatively related to cell wall reinforcement proteins, both of which were shown in previous studies to be water-deficit inducible. Moreover, association in lp5-like involves a nonsynonymous mutation in linkage disequilibrium with two other nonsynonymous polymorphisms that could, by acting together, enhance overall phenotypic effects. This study highlights the complexity of dissecting CID traits and provides insights for designing second-generation association studies based on candidate gene approaches in forest trees.

Interpreting principal component analyses of spatial population genetic variation

Nearly 30 years ago, Cavalli-Sforza et al. pioneered the use of principal component analysis (PCA) in population genetics and used PCA to produce maps summarizing human genetic variation across continental regions. They interpreted gradient and wave patterns in these maps as signatures of specific migration events. These interpretations have been controversial, but influential, and the use of PCA has become widespread in analysis of population genetics data. However, the behavior of PCA for genetic data showing continuous spatial variation, such as might exist within human continental groups, has been less well characterized. Here, we find that gradients and waves observed in Cavalli-Sforza et al.'s maps resemble sinusoidal mathematical artifacts that arise generally when PCA is applied to spatial data, implying that the patterns do not necessarily reflect specific migration events. Our findings aid interpretation of PCA results and suggest how PCA can help correct for continuous population structure in association studies.

When population genetics serves genomics: putting adaptation back in a spatial and historical context

Recent advances in molecular biology have opened new perspectives for the study of plant adaptation, especially at the intraspecific level. Nowadays, scientists employing -omic results in multiple scientific fields can be optimistic of their chances of revealing mechanisms involved in adaptive population divergence. However, the investment required by integrative studies greatly reduces the number of experiments that can be performed. In this context, a comprehensive choice of accessions under study is crucial. We maintain this choice could be appreciably enlightened by population genetics because it helps putting adaptive population divergence in a spatial and historical context. As an example, we highlight the usefulness of knowledge about population genetic structure in the integrative study of metal tolerance in Arabidopsis halleri.

Nucleotide polymorphism and phenotypic associations within and around the phytochrome B2 Locus in European aspen (Populus tremula, Salicaceae)

We investigated the utility of association mapping to dissect the genetic basis of naturally occurring variation in bud phenology in European aspen (Populus tremula). With this aim, we surveyed nucleotide polymorphism in 13 fragments spanning an 80-kb region surrounding the phytochrome B2 (phyB2) locus. Although polymorphism varies substantially across the phyB2 region, we detected no signs for deviations from neutral expectations. We also identified a total of 41 single nucleotide polymorphisms (SNPs) that were subsequently scored in a mapping population consisting of 120 trees. We identified two nonsynonymous SNPs in the phytochrome B2 gene that were independently associated with variation in the timing of bud set and that explained between 1.5 and 5% of the observed phenotypic variation in bud set. Earlier studies have shown that the frequencies of both these SNPs vary clinally with latitude. Linkage disequilibrium across the region was low, suggesting that the SNPs we identified are strong candidates for being causally linked to variation in bud set in our mapping populations. One of the SNPs (T608N) is located in the "hinge region," close to the chromophore binding site of the phyB2 protein. The other SNP (L1078P) is located in a region supposed to mediate downstream signaling from the phyB2 locus. The lack of population structure, combined with low levels of linkage disequilibrium, suggests that association mapping is a fruitful method for dissecting naturally occurring variation in Populus tremula.

eQED: an efficient method for interpreting eQTL associations using protein networks

Analysis of expression quantitative trait loci (eQTLs) is an emerging technique in which individuals are genotyped across a panel of genetic markers and, simultaneously, phenotyped using DNA microarrays. Because of the spacing of markers and linkage disequilibrium, each marker may be near many genes making it difficult to finely map which of these genes are the causal factors responsible for the observed changes in the downstream expression. To address this challenge, we present an efficient method for prioritizing candidate genes at a locus. This approach, called ‘eQTL electrical diagrams' (eQED), integrates eQTLs with protein interaction networks by modeling the two data sets as a wiring diagram of current sources and resistors. eQED achieved a 79% accuracy in recovering a reference set of regulator–target pairs in yeast, which is significantly higher than the performance of three competing methods. eQED also annotates 368 protein–protein interactions with their directionality of information flow with an accuracy of approximately 75%.

Efficient control of population structure in model organism association mapping

Genomewide association mapping in model organisms such as inbred mouse strains is a promising approach for the identification of risk factors related to human diseases. However, genetic association studies in inbred model organisms are confronted by the problem of complex population structure among strains. This induces inflated false positive rates, which cannot be corrected using standard approaches applied in human association studies such as genomic control or structured association. Recent studies demonstrated that mixed models successfully correct for the genetic relatedness in association mapping in maize and Arabidopsis panel data sets. However, the currently available mixed-model methods suffer from computational inefficiency. In this article, we propose a new method, efficient mixed-model association (EMMA), which corrects for population structure and genetic relatedness in model organism association mapping. Our method takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which allows us to substantially increase the computational speed and reliability of the results. We applied EMMA to in silico whole-genome association mapping of inbred mouse strains involving hundreds of thousands of SNPs, in addition to Arabidopsis and maize data sets. We also performed extensive simulation studies to estimate the statistical power of EMMA under various SNP effects, varying degrees of population structure, and differing numbers of multiple measurements per strain. Despite the limited power of inbred mouse association mapping due to the limited number of available inbred strains, we are able to identify significantly associated SNPs, which fall into known QTL or genes identified through previous studies while avoiding an inflation of false positives. An R package implementation and webserver of our EMMA method are publicly available.

Variation in molybdenum content across broadly distributed populations of Arabidopsis thaliana is controlled by a mitochondrial molybdenum transporter (MOT1)

Molybdenum (Mo) is an essential micronutrient for plants, serving as a cofactor for enzymes involved in nitrate assimilation, sulfite detoxification, abscisic acid biosynthesis, and purine degradation. Here we show that natural variation in shoot Mo content across 92 Arabidopsis thaliana accessions is controlled by variation in a mitochondrially localized transporter (Molybdenum Transporter 1 - MOT1) that belongs to the sulfate transporter superfamily. A deletion in the MOT1 promoter is strongly associated with low shoot Mo, occurring in seven of the accessions with the lowest shoot content of Mo. Consistent with the low Mo phenotype, MOT1 expression in low Mo accessions is reduced. Reciprocal grafting experiments demonstrate that the roots of Ler-0 are responsible for the low Mo accumulation in shoot, and GUS localization demonstrates that MOT1 is expressed strongly in the roots. MOT1 contains an N-terminal mitochondrial targeting sequence and expression of MOT1 tagged with GFP in protoplasts and transgenic plants, establishing the mitochondrial localization of this protein. Furthermore, expression of MOT1 specifically enhances Mo accumulation in yeast by 5-fold, consistent with MOT1 functioning as a molybdate transporter. This work provides the first molecular insight into the processes that regulate Mo accumulation in plants and shows that novel loci can be detected by association mapping.

Association mapping of partitioning loci in barley

Background

Association mapping, initially developed in human disease genetics, is now being applied to plant species. The model species Arabidopsis provided some of the first examples of association mapping in plants, identifying previously cloned flowering time genes, despite high population sub-structure. More recently, association genetics has been applied to barley, where breeding activity has resulted in a high degree of population sub-structure. A major genotypic division within barley is that between winter- and spring-sown varieties, which differ in their requirement for vernalization to promote subsequent flowering. To date, all attempts to validate association genetics in barley by identifying major flowering time loci that control vernalization requirement (VRN-H1 and VRN-H2) have failed. Here, we validate the use of association genetics in barley by identifying VRN-H1 and VRN-H2, despite their prominent role in determining population sub-structure.

Results

By taking barley as a typical inbreeding crop, and seasonal growth habit as a major partitioning phenotype, we develop an association mapping approach which successfully identifies VRN-H1 and VRN-H2, the underlying loci largely responsible for this agronomic division. We find a combination of Structured Association followed by Genomic Control to correct for population structure and inflation of the test statistic, resolved significant associations only with VRN-H1 and the VRN-H2 candidate genes, as well as two genes closely linked to VRN-H1 (HvCSFs1 and HvPHYC).

Conclusion

We show that, after employing appropriate statistical methods to correct for population sub-structure, the genome-wide partitioning effect of allelic status at VRN-H1 and VRN-H2 does not result in the high levels of spurious association expected to occur in highly structured samples. Furthermore, we demonstrate that both VRN-H1 and the candidate VRN-H2 genes can be identified using association mapping. Discrimination between intragenic VRN-H1 markers was achieved, indicating that candidate causative polymorphisms may be discerned and prioritised within a larger set of positive associations. This proof of concept study demonstrates the feasibility of association mapping in barley, even within highly structured populations. A major advantage of this method is that it does not require large numbers of genome-wide markers, and is therefore suitable for fine mapping and candidate gene evaluation, especially in species for which large numbers of genetic markers are either unavailable or too costly.

Amino acid polymorphisms in Arabidopsis phytochrome B cause differential responses to light

Plants have a sophisticated system for sensing and responding to their light environment. The light responses of populations and species native to different habitats show adaptive variation; understanding the mechanisms underlying photomorphogenic variation is therefore of significant interest. In Arabidopsis thaliana, phytochrome B (PHYB) is the dominant photoreceptor for red light and plays a major role in white light. Because PHYB has been proposed as a candidate gene for several quantitative trait loci (QTLs) affecting light response, we have investigated sequence and functional variation in Arabidopsis PHYB. We examined PHYB sequences in 33 A. thaliana individuals and in the close relative Arabidopsis lyrata. From 14 nonsynonymous polymorphisms, we chose 5 for further study based on previous QTL studies. In a larger collection of A. thaliana accessions, one of these five polymorphisms, I143L, was associated with variation in red light response. We used transgenic analysis to test this association and confirmed experimentally that natural PHYB polymorphisms cause differential plant responses to light. Furthermore, our results show that allelic variation of PHYB activity is due to amino acid rather than regulatory changes. Together with earlier studies linking variation in light sensitivity to photoreceptor genes, our work suggests that photoreceptors may be a common target of natural selection.

Comparison of mixed-model approaches for association mapping

Association-mapping methods promise to overcome the limitations of linkage-mapping methods. The main objectives of this study were to (i) evaluate various methods for association mapping in the autogamous species wheat using an empirical data set, (ii) determine a marker-based kinship matrix using a restricted maximum-likelihood (REML) estimate of the probability of two alleles at the same locus being identical in state but not identical by descent, and (iii) compare the results of association-mapping approaches based on adjusted entry means (two-step approaches) with the results of approaches in which the phenotypic data analysis and the association analysis were performed in one step (one-step approaches). On the basis of the phenotypic and genotypic data of 303 soft winter wheat (Triticum aestivum L.) inbreds, various association-mapping methods were evaluated. Spearman's rank correlation between P-values calculated on the basis of one- and two-stage association-mapping methods ranged from 0.63 to 0.93. The mixed-model association-mapping approaches using a kinship matrix estimated by REML are more appropriate for association mapping than the recently proposed QK method with respect to (i) the adherence to the nominal alpha-level and (ii) the adjusted power for detection of quantitative trait loci. Furthermore, we showed that our data set could be analyzed by using two-step approaches of the proposed association-mapping method without substantially increasing the empirical type I error rate in comparison to the corresponding one-step approaches.

Adaptations to climate in candidate genes for common metabolic disorders

Evolutionary pressures due to variation in climate play an important role in shaping phenotypic variation among and within species and have been shown to influence variation in phenotypes such as body shape and size among humans. Genes involved in energy metabolism are likely to be central to heat and cold tolerance. To test the hypothesis that climate shaped variation in metabolism genes in humans, we used a bioinformatics approach based on network theory to select 82 candidate genes for common metabolic disorders. We genotyped 873 tag SNPs in these genes in 54 worldwide populations (including the 52 in the Human Genome Diversity Project panel) and found correlations with climate variables using rank correlation analysis and a newly developed method termed Bayesian geographic analysis. In addition, we genotyped 210 carefully matched control SNPs to provide an empirical null distribution for spatial patterns of allele frequency due to population history alone. For nearly all climate variables, we found an excess of genic SNPs in the tail of the distributions of the test statistics compared to the control SNPs, implying that metabolic genes as a group show signals of spatially varying selection. Among our strongest signals were several SNPs (e.g., LEPR R109K, FABP2 A54T) that had previously been associated with phenotypes directly related to cold tolerance. Since variation in climate may be correlated with other aspects of environmental variation, it is possible that some of the signals that we detected reflect selective pressures other than climate. Nevertheless, our results are consistent with the idea that climate has been an important selective pressure acting on candidate genes for common metabolic disorders.

Ionomics and the study of the plant ionome

The ionome is defined as the mineral nutrient and trace element composition of an organism and represents the inorganic component of cellular and organismal systems. Ionomics, the study of the ionome, involves the quantitative and simultaneous measurement of the elemental composition of living organisms and changes in this composition in response to physiological stimuli, developmental state, and genetic modifications. Ionomics requires the application of high-throughput elemental analysis technologies and their integration with both bioinformatic and genetic tools. Ionomics has the ability to capture information about the functional state of an organism under different conditions, driven by genetic and developmental differences and by biotic and abiotic factors. The relatively high throughput and low cost of ionomic analysis means that it has the potential to provide a powerful approach to not only the functional analysis of the genes and gene networks that directly control the ionome, but also to the more extended gene networks that control developmental and physiological processes that affect the ionome indirectly. In this review we describe the analytical and bioinformatics aspects of ionomics, as well as its application as a functional genomics tool.

Genome-wide association mapping using mixed-models: application to GAW15 Problem 3

We apply an analysis based upon mixed-models to the Genetic Analysis Workshop 15, Problem 3 simulated data. Such models are commonly used to mitigate the tendency for population structure, or cryptic relatedness, to inflate the false-positive rate of test statistics. They also allow for explicit modeling of varying degrees of relatedness in samples in which some individuals are related by (possibly unknown) pedigree, whereas others are not. Furthermore, the implementation of the method we describe here is quick enough to be used effectively on genome-wide data. We present an analysis of the data for Genetic Analysis Workshop 15, Problem 3, in which we show that these methods can effectively find signals in this data. Somewhat disappointingly, the false-positive rate does not appear to be reduced, but this is largely because the method used to simulate the data appears not to have encompassed effects, such as population stratification, that might have led to inflation of p-values.

Livestock genomics: bridging the gap between mice and men

Dissecting the genetic control of variation in complex traits, such as disease resistance and agricultural-product quality, remains very challenging. Farm animals are now well placed to bridge the gap between human biology and traditional model species. Livestock species share with model species the benefits of controlled breeding, and their biology is often much closer to that of humans. Genetic research in model species focuses on differences between homogenous lines, whereas genetic research in humans focuses on genetic variation within populations. Livestock genetics has the strengths of both human and model-species genetics because researchers can exploit both the abundant genetic variation between divergent breeds and the variation that is segregating within breeds. Therefore, livestock genomics fills the void where the genetics of model species proves intractable or where model species are not a good proxy for human biology.

Major regulatory genes in maize contribute to standing variation in teosinte (Zea mays ssp. parviglumis)

In plants, many major regulatory genes that control plant growth and development have been identified and characterized. Despite a detailed knowledge of the function of these genes little is known about how they contribute to the natural variation for complex traits. To determine whether major regulatory genes of maize contribute to standing variation in Balsas teosinte we conducted association mapping in 584 Balsas teosinte individuals. We tested 48 markers from nine candidate regulatory genes against 13 traits for plant and inflorescence architecture. We identified significant associations using a mixed linear model that controls for multiple levels of relatedness. Ten associations involving five candidate genes were significant after correction for multiple testing, and two survive the conservative Bonferroni correction. zfl2, the maize homolog of FLORICAULA of Antirrhinum, was associated with plant height. zap1, the maize homolog of APETALA1 of Arabidopsis, was associated with inflorescence branching. Five SNPs in the maize domestication gene, teosinte branched1, were significantly associated with either plant or inflorescence architecture. Our data suggest that major regulatory genes in maize do play a role in the natural variation for complex traits in teosinte and that some of the minor variants we identified may have been targets of selection during domestication.

Differential expression of genes important for adaptation in Capsella bursa-pastoris (Brassicaceae)

Understanding the genetic basis of natural variation is of primary interest for evolutionary studies of adaptation. In Capsella bursa-pastoris, a close relative of Arabidopsis (Arabidopsis thaliana), variation in flowering time is correlated with latitude, suggestive of an adaptation to photoperiod. To identify pathways regulating natural flowering time variation in C. bursa-pastoris, we have studied gene expression differences between two pairs of early- and late-flowering C. bursa-pastoris accessions and compared their response to vernalization. Using Arabidopsis microarrays, we found a large number of significant differences in gene expression between flowering ecotypes. The key flowering time gene FLOWERING LOCUS C (FLC) was not differentially expressed prior to vernalization. This result is in contrast to those in Arabidopsis, where most natural flowering time variation acts through FLC. However, the gibberellin and photoperiodic flowering pathways were significantly enriched for gene expression differences between early- and late-flowering C. bursa-pastoris. Gibberellin biosynthesis genes were down-regulated in late-flowering accessions, whereas circadian core genes in the photoperiodic pathway were differentially expressed between early- and late-flowering accessions. Detailed time-series experiments clearly demonstrated that the diurnal rhythm of CIRCADIAN CLOCK-ASSOCIATED1 (CCA1) and TIMING OF CAB EXPRESSION1 (TOC1) expression differed between flowering ecotypes, both under constant light and long-day conditions. Differential expression of flowering time genes was biologically validated in an independent pair of flowering ecotypes, suggesting a shared genetic basis or parallel evolution of similar regulatory differences. We conclude that genes involved in regulation of the circadian clock, such as CCA1 and TOC1, are strong candidates for the evolution of adaptive flowering time variation in C. bursa-pastoris.

Description and applications of a rapid and sensitive non-radioactive microplate-based assay for maximum and initial activity of D-ribulose-1,5-bisphosphate carboxylase/oxygenase

D-ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) catalyses the first step in photosynthetic carbon assimilation and represents the largest sink for nitrogen in plants. Improvement of its kinetic properties or the efficiency with which it is used in planta would benefit photosynthesis, nitrogen and water use efficiency, and yield. This paper presents a new non-radioactive microplate-based assay, which determines the product [3-phosphoglycerate (3-PGA)] in an enzymic cycle between glycerol-3-phosphate dehydrogenase and glycerol-3-phosphate oxidase. High sensitivity permits use of highly diluted extracts, and a short reaction time to avoid problems due to fall-off. Throughput was several hundreds of samples per person per day. Sensitivity and convenience compared favourably with radioisotopic assays, which were previously used to assay Rubisco. Its use is illustrated in three applications. (1) Maximal and initial activities and the K(m) for ribulose-1,5-bisphosphate were determined in raw extracts of leaves from several species. Similar values were obtained from those in the literature. (2) Diurnal changes were compared in rosettes of wild-type (WT) Arabidopsis and the starchless pgm mutant. Despite these dramatic differences in carbon metabolism, Rubisco activity and activation were similar in both genotypes. (3) A preliminary association mapping study was performed with 118 Arabidopsis accessions, using 183 markers that probably cover approximately 3-8% of the total genome. At a P-value < 0.005, two, two and no quantitative trait loci (QTL) were found for Rubisco maximal activity, initial activity and activation state, respectively. Inspection of the genomic regions that span these markers revealed these QTL involved genes not previously implicated in the regulation of Rubisco expression or activity.

Large-scale in silico mapping of complex quantitative traits in inbred mice

Understanding the genetic basis of common disease and disease-related quantitative traits will aid in the development of diagnostics and therapeutics. The processs of gene discovery can be sped up by rapid and effective integration of well-defined mouse genome and phenome data resources. We describe here an in silico gene-discovery strategy through genome-wide association (GWA) scans in inbred mice with a wide range of genetic variation. We identified 937 quantitative trait loci (QTLs) from a survey of 173 mouse phenotypes, which include models of human disease (atherosclerosis, cardiovascular disease, cancer and obesity) as well as behavioral, hematological, immunological, metabolic, and neurological traits. 67% of QTLs were refined into genomic regions <0.5 Mb with ∼40-fold increase in mapping precision as compared with classical linkage analysis. This makes for more efficient identification of the genes that underlie disease. We have identified two QTL genes, Adam12 and Cdh2, as causal genetic variants for atherogenic diet-induced obesity. Our findings demonstrate that GWA analysis in mice has the potential to resolve multiple tightly linked QTLs and achieve single-gene resolution. These high-resolution QTL data can serve as a primary resource for positional cloning and gene identification in the research community.

TASSEL: software for association mapping of complex traits in diverse samples

Association analyses that exploit the natural diversity of a genome to map at very high resolutions are becoming increasingly important. In most studies, however, researchers must contend with the confounding effects of both population and family structure. TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure. For result interpretation, the program allows for linkage disequilibrium statistics to be calculated and visualized graphically. Database browsing and data importation is facilitated by integrated middleware. Other features include analyzing insertions/deletions, calculating diversity statistics, integration of phenotypic and genotypic data, imputing missing data and calculating principal components.

The genetic architecture of shoot branching in Arabidopsis thaliana: a comparative assessment of candidate gene associations vs. quantitative trait locus mapping

Association mapping focused on 36 genes involved in branch development was used to identify candidate genes for variation in shoot branching in Arabidopsis thaliana. The associations between four branching traits and moderate-frequency haplogroups at the studied genes were tested in a panel of 96 accessions from a restricted geographic range in Central Europe. Using a mixed-model association-mapping method, we identified three loci--MORE AXILLARY GROWTH 2 (MAX2), MORE AXILLARY GROWTH 3 (MAX3), and SUPERSHOOT 1 (SPS1)--that were significantly associated with branching variation. On the basis of a more extensive examination of the MAX2 and MAX3 genomic regions, we find that linkage disequilibrium in these regions decays within approximately 10 kb and trait associations localize to the candidate genes in these regions. When the significant associations are compared to relevant quantitative trait loci (QTL) from previous Ler x Col and Cvi x Ler recombinant inbred line (RIL) mapping studies, no additive QTL overlapping these candidate genes are observed, although epistatic QTL for branching, including one that spans the SPS1, are found. These results suggest that epistasis is prevalent in determining branching variation in A. thaliana and may need to be considered in linkage disequilibrium mapping studies of genetically diverse accessions.