Polymorphisms that affect complex traits or quantitative trait loci (QTL) often affect multiple traits. We describe two novel methods (1) for finding single nucleotide polymorphisms (SNPs) significantly associated with one or more traits using a multi-trait, meta-analysis, and (2) for distinguishing between a single pleiotropic QTL and multiple linked QTL. The meta-analysis uses the effect of each SNP on each of n traits, estimated in single trait genome wide association studies (GWAS). These effects are expressed as a vector of signed t-values (t) and the error covariance matrix of these t values is approximated by the correlation matrix of t-values among the traits calculated across the SNP (V). Consequently, t'V-1t is approximately distributed as a chi-squared with n degrees of freedom. An attractive feature of the meta-analysis is that it uses estimated effects of SNPs from single trait GWAS, so it can be applied to published data where individual records are not available. We demonstrate that the multi-trait method can be used to increase the power (numbers of SNPs validated in an independent population) of GWAS in a beef cattle data set including 10,191 animals genotyped for 729,068 SNPs with 32 traits recorded, including growth and reproduction traits. We can distinguish between a single pleiotropic QTL and multiple linked QTL because multiple SNPs tagging the same QTL show the same pattern of effects across traits. We confirm this finding by demonstrating that when one SNP is included in the statistical model the other SNPs have a non-significant effect. In the beef cattle data set, cluster analysis yielded four groups of QTL with similar patterns of effects across traits within a group. A linear index was used to validate SNPs having effects on multiple traits and to identify additional SNPs belonging to these four groups.

BACKGROUND: Transcription factors (TFs) are vital elements that regulate transcription and the spatio-temporal expression of genes, thereby ensuring the accurate development and functioning of an organism. The identification of TF-encoding genes in a liverwort, Marchantia polymorpha, offers insights into TF organization in the members of the most basal lineages of land plants (embryophytes). Therefore, a comparison of Marchantia TF genes with other land plants (monocots, dicots, bryophytes) and algae (chlorophytes, rhodophytes) provides the most comprehensive view of the rates of expansion or contraction of TF genes in plant evolution. RESULTS: In this study, we report the identification of TF-encoding transcripts in M. polymorpha for the first time, as evidenced by deep RNA sequencing data. In total, 3,471 putative TF encoding transcripts, distributed in 80 families, were identified, representing 7.4% of the generated Marchantia gametophytic transcriptome dataset. Overall, TF basic functions and distribution across families appear to be conserved when compared to other plant species. However, it is of interest to observe the genesis of novel sequences in 24 TF families and the apparent termination of 2 TF families with the emergence of Marchantia. Out of 24 TF families, 6 are known to be associated with plant reproductive development processes. We also examined the expression pattern of these TF-encoding transcripts in six male and female developmental stages in vegetative and reproductive gametophytic tissues of Marchantia. CONCLUSIONS: The analysis highlighted the importance of Marchantia, a model plant system, in an evolutionary context. The dataset generated here provides a scientific resource for TF gene discovery and other comparative evolutionary studies of land plants.

CLAVATA3 (CLV3), a stem cell marker in Arabidopsis thaliana, encodes a secreted peptide that maintains the stem cell population within the shoot apical meristem. This work investigated the CLV3 orthologue in a major legume crop, soybean (GmCLV3). Instead of being expressed in the three outermost layers of the meristem as in Arabidopsis, GmCLV3 was expressed deeper in the central zone beneath the fourth layer (L4) of the meristem, overlapping with the expression of soybean WUSCHEL. Subsequent investigation using an alternative stem cell marker (GmLOG1) revealed its expression within layers L2-L4, indicating that GmCLV3 is not a stem cell marker. Overexpression studies of GmCLV3 in Arabidopsis and complementation of clv3-2 mutant suggest similar functional capacity to that of Arabidopsis CLV3. The expression of soybean CLV1, which encodes a receptor for CLV3 in Arabidopsis, was not detectable in the central zone of the meristem via reverse-transcription PCR analysis of amplified RNA from laser-microdissected samples or in situ, implicating a diverged pathway in soybean. This study also reports the novel expression of GmLOG1 in initials of axillary meristem in the boundary region between the SAM and developing leaf primordia, before the expression of GmWUS or GmCLV3, indicating cytokinin as one of the earliest signals in initiating and specifying the stem cell population.

BACKGROUND: Field pea (Pisum sativum L.) is a self-pollinating, diploid, cool-season food legume. Crop production is constrained by multiple biotic and abiotic stress factors, including salinity, that cause reduced growth and yield. Recent advances in genomics have permitted the development of low-cost high-throughput genotyping systems, allowing the construction of saturated genetic linkage maps for identification of quantitative trait loci (QTLs) associated with traits of interest. Genetic markers in close linkage with the relevant genomic regions may then be implemented in varietal improvement programs. RESULTS: In this study, single nucleotide polymorphism (SNP) markers associated with expressed sequence tags (ESTs) were developed and used to generate comprehensive linkage maps for field pea. From a set of 36,188 variant nucleotide positions detected through in silico analysis, 768 were selected for genotyping of a recombinant inbred line (RIL) population. A total of 705 SNPs (91.7%) successfully detected segregating polymorphisms. In addition to SNPs, genomic and EST-derived simple sequence repeats (SSRs) were assigned to the genetic map in order to obtain an evenly distributed genome-wide coverage. Sequences associated with the mapped molecular markers were used for comparative genomic analysis with other legume species. Higher levels of conserved synteny were observed with the genomes of Medicago truncatula Gaertn. and chickpea (Cicer arietinum L.) than with soybean (Glycine max [L.] Merr.), Lotus japonicus L. and pigeon pea (Cajanus cajan [L.] Millsp.). Parents and RIL progeny were screened at the seedling growth stage for responses to salinity stress, imposed by addition of NaCl in the watering solution at a concentration of 18 dS m-1. Salinity-induced symptoms showed normal distribution, and the severity of the symptoms increased over time. QTLs for salinity tolerance were identified on linkage groups Ps III and VII, with flanking SNP markers suitable for selection of resistant cultivars. Comparison of sequences underpinning these SNP markers to the M. truncatula genome defined genomic regions containing candidate genes associated with saline stress tolerance. CONCLUSION: The SNP assays and associated genetic linkage maps developed in this study permitted identification of salinity tolerance QTLs and candidate genes. This constitutes an important set of tools for marker-assisted selection (MAS) programs aimed at performance enhancement of field pea cultivars.

BACKGROUND: The apparent effect of a single nucleotide polymorphism (SNP) on phenotype depends on the linkage disequilibrium (LD) between the SNP and a quantitative trait locus (QTL). However, the phase of LD between a SNP and a QTL may differ between Bos indicus and Bos taurus because they diverged at least one hundred thousand years ago. Here, we test the hypothesis that the apparent effect of a SNP on a quantitative trait depends on whether the SNP allele is inherited from a Bos taurus or Bos indicus ancestor. METHODS: Phenotype data on one or more traits and SNP genotype data for 10 181 cattle from Bos taurus, Bos indicus and composite breeds were used. All animals had genotypes for 729 068 SNPs (real or imputed). Chromosome segments were classified as originating from B. indicus or B. taurus on the basis of the haplotype of SNP alleles they contained. Consequently, SNP alleles were classified according to their sub-species origin. Three models were used for the association study: (1) conventional GWAS (genome-wide association study), fitting a single SNP effect regardless of subspecies origin, (2) interaction GWAS, fitting an interaction between SNP and subspecies-origin, and (3) best variable GWAS, fitting the most significant combination of SNP and sub-species origin. RESULTS: Fitting an interaction between SNP and subspecies origin resulted in more significant SNPs (i.e. more power) than a conventional GWAS. Thus, the effect of a SNP depends on the subspecies that the allele originates from. Also, most QTL segregated in only one subspecies, suggesting that many mutations that affect the traits studied occurred after divergence of the subspecies or the mutation became fixed or was lost in one of the subspecies. CONCLUSIONS: The results imply that GWAS and genomic selection could gain power by distinguishing SNP alleles based on their subspecies origin, and that only few QTL segregate in both B. indicus and B. taurus cattle. Thus, the QTL that segregate in current populations likely resulted from mutations that occurred in one of the subspecies and can have both positive and negative effects on the traits. There was no evidence that selection has increased the frequency of alleles that increase body weight.

BACKGROUND: The classical (C) MIKC-type MADS-box transcription factors comprise one gene family that plays diverse roles in the flowering process ranging from floral initiation to the development of floral organs. Despite their importance in regulating developmental processes that impact crop yield, they remain largely unexplored in the major legume oilseed crop, soybean. RESULTS: We identified 57 MIKC(c)-type transcription factors from soybean and determined the in silico gene expression profiles of the soybean MIKC(c)-type genes across different tissues. Our study implicates three MIKC(c)-type transcription factors as novel members of the AGAMOUS LIKE 6 (AGL6) subfamily of the MIKC(C)-type MADS-box genes, and we named this sister clade PsMADS3. While similar genes were identified in other legume species, poplar and grape, no such gene is represented in Arabidopsis thaliana or rice. RT-PCR analysis on these three soybean PsMADS3 genes during early floral initiation processes revealed their temporal expression similar to that of APETALA1, a gene known to function as a floral meristem identity gene. However, RNA in situ hybridisation showed that their spatial expression patterns are markedly different from those of APETALA1. CONCLUSION: Legume flower development system differs from that in the model plant, Arabidopsis. There is an overlap in the initiation of different floral whorls in soybean, and inflorescent meristems can revert to leaf production depending on the environmental conditions. MIKC(C)-type MADS-box genes have been shown to play key regulatory roles in different stages of flower development. We identified members of the PsMADS3 sub-clade in legumes that show differential spatial expression during floral initiation, indicating their potential novel roles in the floral initiation process. The results from this study will contribute to a better understanding of legume-specific floral developmental processes.

Flowering process governs seed set and thus affects agricultural productivity. Soybean, a major legume crop, requires short-day photoperiod conditions for flowering. While leaf-derived signal(s) are essential for the photoperiod-induced floral initiation process at the shoot apical meristem, molecular events associated with early floral transition stages in either leaves or shoot apical meristems are not well understood. To provide novel insights into the molecular basis of floral initiation, RNA-Seq was used to characterize the soybean transcriptome of leaf and micro-dissected shoot apical meristem at different time points after short-day treatment. Shoot apical meristem expressed a higher number of transcripts in comparison to that of leaf highlighting greater diversity and abundance of transcripts expressed in the shoot apical meristem. A total of 2951 shoot apical meristem and 13,609 leaf sequences with significant profile changes during the time course examined were identified. Most changes in mRNA level occurred after 1short-day treatment. Transcripts involved in mediating responses to stimulus including hormones or in various metabolic processes represent the top enriched GO functional category for the SAM and leaf dataset, respectively. Transcripts associated with protein degradation were also significantly changing in leaf and SAM implicating their involvement in triggering the developmental switch. RNA-Seq analysis of shoot apical meristem and leaf from soybean undergoing floral transition reveal major reprogramming events in leaves and the SAM that point toward hormones gibberellins (GA) and cytokinin as key regulators in the production of systemic flowering signal(s) in leaves. These hormones may form part of the systemic signals in addition to the established florigen, FLOWERING LOCUS T (FT). Further, evidence is emerging that the conversion of shoot apical meristem to inflorescence meristem is linked with the interplay of auxin, cytokinin and GA creating a low cytokinin and high GA environment.

BACKGROUND: Hydroponic growth systems are a convenient platform for studying whole plant physiology. However, we found through trialling systems as they are described in the literature that our experiments were frequently confounded by factors that affected plant growth, including algal contamination and hypoxia. We also found the way in which the plants were grown made them poorly amenable to a number of common physiological assays. RESULTS: The drivers for the development of this hydroponic system were: 1) the exclusion of light from the growth solution; 2) to simplify the handling of individual plants, and 3) the growth of the plant to allow easy implementation of multiple assays. These aims were all met by the use of pierced lids of black microcentrifuge tubes. Seed was germinated on a lid filled with an agar-containing germination media immersed in the same solution. Following germination, the liquid growth media was exchanged with the experimental solution, and after 14-21 days seedlings were transferred to larger tanks with aerated solution where they remained until experimentation. We provide details of the protocol including composition of the basal growth solution, and separate solutions with altered calcium, magnesium, potassium or sodium supply whilst maintaining the activity of the majority of other ions. We demonstrate the adaptability of this system for: gas exchange measurement on single leaves and whole plants; qRT-PCR to probe the transcriptional response of roots or shoots to altered nutrient composition in the growth solution (we demonstrate this using high and low calcium supply); producing highly competent mesophyll protoplasts; and, accelerating the screening of Arabidopsis transformants. This system is also ideal for manipulating plants for micropipette techniques such as electrophysiology or SiCSA. CONCLUSIONS: We present an optimised plant hydroponic culture system that can be quickly and cheaply constructed, and produces plants with similar growth kinetics to soil-grown plants, but with the advantage of being a versatile platform for a myriad of physiological and molecular biological measurements on all plant tissues at all developmental stages. We present 'tips and tricks' for the easy adoption of this hydroponic culture system.

BACKGROUND: The accuracy of genomic prediction is highly dependent on the size of the reference population. For small populations, including information from other populations could improve this accuracy. The usual strategy is to pool data from different populations; however, this has not proven as successful as hoped for with distantly related breeds. BayesRS is a novel approach to share information across populations for genomic predictions. The approach allows information to be captured even where the phase of SNP alleles and casuative mutation alleles are reversed across populations, or the actual casuative mutation is different between the populations but affects the same gene. Proportions of a four-distribution mixture for SNP effects in segments of fixed size along the genome are derived from one population and set as location specific prior proportions of distributions of SNP effects for the target population. The model was tested using dairy cattle populations of different breeds: 540 Australian Jersey bulls, 2297 Australian Holstein bulls and 5214 Nordic Holstein bulls. The traits studied were protein-, fat- and milk yield. Genotypic data was Illumina 777K SNPs, real or imputed. RESULTS: Results showed an increase in accuracy of up to 3.5% for the Jersey population when using BayesRS with a prior derived from Australian Holstein compared to a model without location specific priors. The increase in accuracy was however lower than was achieved when reference populations were combined to estimate SNP effects, except in the case of fat yield. The small size of the Jersey validation set meant that these improvements in accuracy were not significant using a Hotelling-Williams t-test at the 5% level. An increase in accuracy of 1-2% for all traits was observed in the Australian Holstein population when using a prior derived from the Nordic Holstein population compared to using no prior information. These improvements were significant (P<0.05) using the Hotelling Williams t-test for protein- and fat yield. CONCLUSION: For some traits the method might be advantageous compared to pooling of reference data for distantly related populations, but further investigation is needed to confirm the results. For closely related populations the method does not perform better than pooling reference data. However, it does give an increased accuracy compared to analysis based on only one reference population, without an increased computational burden. The approach described here provides a general setup for inclusion of location specific priors: the approach could be used to include biological information in genomic predictions.

Whole-genome sequence is potentially the richest source of genetic data for inferring ancestral demography. However, full sequence also presents significant challenges to fully utilize such large data sets and to ensure that sequencing errors do not introduce bias into the inferred demography. Using whole-genome sequence data from two Holstein cattle, we demonstrate a new method to correct for bias caused by hidden errors and then infer stepwise changes in ancestral demography up to present. There was a strong upward bias in estimates of recent effective population size (Ne) if the correction method was not applied to the data, both for our method and the Li and Durbin (Inference of human population history from individual whole-genome sequences. Nature 475:493-496) pairwise sequentially Markovian coalescent method. To infer demography, we use an analytical predictor of multiloci linkage disequilibrium (LD) based on a simple coalescent model that allows for changes in Ne. The LD statistic summarizes the distribution of runs of homozygosity for any given demography. We infer a best fit demography as one that predicts a match with the observed distribution of runs of homozygosity in the corrected sequence data. We use multiloci LD because it potentially holds more information about ancestral demography than pairwise LD. The inferred demography indicates a strong reduction in the Ne around 170,000 years ago, possibly related to the divergence of African and European Bos taurus cattle. This is followed by a further reduction coinciding with the period of cattle domestication, with Ne of between 3,500 and 6,000. The most recent reduction of Ne to approximately 100 in the Holstein breed agrees well with estimates from pedigrees. Our approach can be applied to whole-genome sequence from any diploid species and can be scaled up to use sequence from multiple individuals.

Concerns about increasing concentrations of greenhouse gases in the atmosphere, primarily carbon dioxide (CO2), have raised worldwide interest in the potential of agricultural soils to be carbon (C) sinks. In Australia, studies that have quantified the effects of improved management practices in croplands on soil C have generally been inconclusive and contradictory for different soil depths and durations of the management changes. We therefore quantitatively synthesised the results of Australian studies using meta-analytic techniques to assess the technical and economic feasibility of increasing the soil C stock by improved management practices. Our results indicate that the potential of these improved practices to store C is limited to the surface 0-10 cm of soil and diminishes with time. None of these widely adopted practices is currently financially attractive under Australia's new legislation known as the Carbon Farming Initiative.

BACKGROUND: Breeding livestock for improved resistance to disease is an increasingly important selection goal. However, the risk of pathogens adapting to livestock bred for improved disease resistance is difficult to quantify. Here, we explore the possibility of gastrointestinal worms adapting to sheep bred for low faecal worm egg count using computer simulation. Our model assumes sheep and worm genotypes interact at a single locus, such that the effect of an A allele in sheep is dependent on worm genotype, and the B allele in worms is favourable for parasitizing the A allele sheep but may increase mortality on pasture. We describe the requirements for adaptation and test if worm adaptation (1) is slowed by non-genetic features of worm infections and (2) can occur with little observable change in faecal worm egg count. RESULTS: Adaptation in worms was found to be primarily influenced by overall worm fitness, viz. the balance between the advantage of the B allele during the parasitic stage in sheep and its disadvantage on pasture. Genetic variation at the interacting locus in worms could be from de novo or segregating mutations, but de novo mutations are rare and segregating mutations are likely constrained to have (near) neutral effects on worm fitness. Most other aspects of the worm infection we modelled did not affect the outcomes. However, the host-controlled mechanism to reduce faecal worm egg count by lowering worm fecundity reduced the selection pressure on worms to adapt compared to other mechanisms, such as increasing worm mortality. Temporal changes in worm egg count were unreliable for detecting adaptation, despite the steady environment assumed in the simulations. CONCLUSIONS: Adaptation of worms to sheep selected for low faecal worm egg count requires an allele segregating in worms that is favourable in animals with improved resistance but less favourable in other animals. Obtaining alleles with this specific property seems unlikely. With support from experimental data, we conclude that selection for low faecal worm egg count should be stable over a short time frame (e.g. 20 years). We are further exploring model outcomes with multiple loci and comparing outcomes to other control strategies.