Development of a QTL-environment-based predictive model for node addition rate in common bean

To select a plant genotype that will thrive in targeted environments it is critical to understand the genotype by environment interaction (GEI). In this study, multi-environment QTL analysis was used to characterize node addition rate (NAR, node day− 1) on the main stem of the common bean (Phaseolus vulgaris L). This analysis was carried out with field data of 171 recombinant inbred lines that were grown at five sites (Florida, Puerto Rico, 2 sites in Colombia, and North Dakota). Four QTLs (Nar1, Nar2, Nar3 and Nar4) were identified, one of which had significant QTL by environment interactions (QEI), that is, Nar2 with temperature. Temperature was identified as the main environmental factor affecting NAR while day length and solar radiation played a minor role. Integration of sites as covariates into a QTL mixed site-effect model, and further replacing the site component with explanatory environmental covariates (i.e., temperature, day length and solar radiation) yielded a model that explained 73% of the phenotypic variation for NAR with root mean square error of 16.25% of the mean. The QTL consistency and stability was examined through a tenfold cross validation with different sets of genotypes and these four QTLs were always detected with 50–90% probability. The final model was evaluated using leave-one-site-out method to assess the influence of site on node addition rate. These analyses provided a quantitative measure of the effects on NAR of common beans exerted by the genetic makeup, the environment and their interactions

Punctuated distribution of recombination hotspots and demarcation of pericentromeric regions in Phaseolus vulgaris L.

High density genetic maps are a reliable tool for genetic dissection of complex plant traits. Mapping resolution is often hampered by the variable crossover and non-crossover events occurring across the genome, with pericentromeric regions (pCENR) showing highly suppressed recombination rates. The efficiency of linkage mapping can further be improved by characterizing and understanding the distribution of recombinational activity along individual chromosomes. In order to evaluate the genome wide recombination rate in common beans (Phaseolus vulgaris L.) we developed a SNP-based linkage map using the genotype-by-sequencing approach with a 188 recombinant inbred line family generated from an inter gene pool cross (Andean x Mesoamerican). We identified 1,112 SNPs that were subsequently used to construct a robust linkage map with 11 groups, comprising 513 recombinationally unique marker loci spanning 943 cM (LOD 3.0). Comparative analysis showed that the linkage map spanned >95% of the physical map, indicating that the map is almost saturated. Evaluation of genome-wide recombination rate indicated that at least 45% of the genome is highly recombinationally suppressed, and allowed us to estimate locations of pCENRs. We observed an average recombination rate of 0.25 cM/Mb in pCENRs as compared to the rest of genome that showed 3.72 cM/Mb. However, several hot spots of recombination were also detected with recombination rates reaching as high as 34 cM/Mb. Hotspots were mostly found towards the end of chromosomes, which also happened to be gene-rich regions. Analyzing relationships between linkage and physical map indicated a punctuated distribution of recombinational hot spots across the genome

Summarized comparison between linkage and physical maps of each chromosome.

<p>“NA”: Data not available</p><p>Summarized comparison between linkage and physical maps of each chromosome.</p

Scatter plot displaying the relationship between centiMorgan distances (left Y-axis) and physical distances (X-axis, Mb) along the 11 chromosomes of <i>P. vulgaris</i> L.

<p>Black circles represent mapped markers (513+739). Red dotted line connects neighboring markers for visual ease. First order differential plot (grey density plot) represent cross-over rate across a given chromosome (right Y-axis, cM/Mb).</p

Circos plot depicting the distribution of various genomic attributes along the physical map of the 11 <i>P. vulgaris</i> chromosomes.

<p>(A) Physical chromosome (Mb) of common bean. (B) Genome wide heat plot of potential <i>Pst</i>I sites detected by <i>in-situ</i> analysis. (C, D) Frequency distribution of Jamapa (blue) and Calima (red) reads obtained through GBS, (y-axis: range 0–160 reads/Mb). (E) Position of GBS-based SNPs identified between parental lines. (F) Physical location of 513 recombinationally unique markers utilized to construct the linkage map. (G) Heat plots highlighting regions with transmission ratio distortions in favor of Jamapa (Blue) or Calima (Red) alleles. (H) Density plot of recombination rate (y-axis: 0–35 cM/Mb). (I) Estimated pericentromeric region (light blue). (J) Density plot of coding sequences (CS) along chromosomes (y-axis: 0–178 CS). (K) Frequency distribution of genome-wide <i>in-situ</i> identified SNPs between Jamapa and Calima (y-axis: 0–2000 SNPs).</p

Linkage map (LOD 3.0) of <i>Phaseolus vulgaris</i> L. with 513 unique marker loci distributed among the 11 chromosomes with an average inter locus distance of 1.84 cM.

<p>Linkage map (LOD 3.0) of <i>Phaseolus vulgaris</i> L. with 513 unique marker loci distributed among the 11 chromosomes with an average inter locus distance of 1.84 cM.</p

Flow chart illustrating bioinformatics steps used to generate the genotype database for the parental (Calima and Jamapa) and RI lines.

<p>Raw sequence data obtained through genotyping-by-sequencing approach was processed in three stages. Stage 1 represent filtration steps and quality check of raw data. Stage 2 depicts separation of sequenced reads using barcode and generation of reference reads set. Stage 3 represent the SNP calling and genotype database generation steps.</p

Distribution of markers on individual linkage groups.

<p>^<i>a</i> Reference chromosome</p><p>^<i>b</i> Linkage group [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116822#pone.0116822.ref014" target="_blank">14</a>]</p><p>Distribution of markers on individual linkage groups.</p

A Predictive Model for Time-to-Flowering in the Common Bean Based on QTL and Environmental Variables

The common bean is a tropical facultative short day legume that is now grown in tropical and temperate zones. This observation underscores how domestication and modern breeding can change the adaptive phenology of a species. A key adaptive trait is the optimal timing of the transition from the vegetative to the reproductive stage. This trait is responsive to genetically controlled signal transduction pathways and local climatic cues. A comprehensive characterization of this trait can be started by assessing the quantitative contribution of the genetic and environmental factors, and their interactions. This study aimed to locate significant QTL (G) and environmental (E) factors controlling time-to-flower in the common bean, and to identify and measure G x E interactions. Phenotypic data were collected from a bi-parental [Andean x Mesoamerican] recombinant inbred population (F11:14, 188 genotypes) grown at five environmentally distinct sites. QTL analysis using a dense linkage map revealed 12 QTL, five of which showed significant interactions with the environment. Dissection of G x E interactions using a linear mixed-effect model revealed that temperature, solar radiation, and photoperiod play major roles in controlling common bean flowering time directly, and indirectly by modifying the effect of certain QTLs. The model predicts flowering time across five sites with an adjusted r-square of 0.89 and root-mean square error of 2.52 days. The model provides the means to disentangle the environmental dependencies of complex traits, and presents an opportunity to identify in-silico QTL allele combinations that could yield desired phenotypes under different climatic conditions