The West Indian fruit fly,Anastrepha obliqua, is an important agricultural pest in the New World. The use of pesticide-free methods to control invasive species such as this reinforces the search for genes potentially useful in their genetic control. Therefore, the study of chemosensory proteins involved with a range of responses to the chemical environment will help not only on the understanding of the species biology but may also help the development of environmentally friendly pest control strategies. Here we analyzed the expression patterns of three OBP genes,Obp19d_2,Obp56aandObp99c, across different phases ofA. obliquadevelopment byqPCR. In order to do so, we tested eight and identified three reference genes for data normalization,rpl17,rpl18andef1a, which displayed stability for the conditions here tested. All OBPs showed differential expression on adults and some differential expression among adult stages.Obp99chad an almost exclusive expression in males andObp56ashowed high expression in virgin females. Thereby, our results provide relevant data not only for other gene expression studies in this species, as well as for the search of candidate genes that may help in the development of new pest control strategies.

Background: Genotyping-by-sequencing (GBS) provides affordable methods for genotyping hundreds of individuals using millions of markers. However, this challenges bioinformatic procedures that must overcome possible artifacts such as the bias generated by polymerase chain reaction duplicates and sequencing errors. Genotyping errors lead to data that deviate from what is expected from regular meiosis. This, in turn, leads to difficulties in grouping and ordering markers, resulting in inflated and incorrect linkage maps. Therefore, genotyping errors can be easily detected by linkage map quality evaluations. Results: We developed and used the Reads2Map workflow to build linkage maps with simulated and empirical GBS data of diploid outcrossing populations. The workflows run GATK, Stacks, TASSEL, and Freebayes for single-nucleotide polymorphism calling and updog, polyRAD, and SuperMASSA for genotype calling, as well as OneMap and GUSMap to build linkage maps. Using simulated data, we observed which genotype call software fails in identifying common errors in GBS sequencing data and proposed specific filters to better handle them. We tested whether it is possible to overcome errors in a linkage map using genotype probabilities from each software or global error rates to estimate genetic distances with an updated version of OneMap. We also evaluated the impact of segregation distortion, contaminant samples, and haplotype-based multiallelic markers in the final linkage maps. Through our evaluations, we observed that some of the approaches produce different results depending on the dataset (dataset dependent) and others produce consistent advantageous results among them (dataset independent). Conclusions: We set as default in the Reads2Map workflows the approaches that showed to be dataset independent for GBS datasets according to our results. This reduces the number of required tests to identify optimal pipelines and parameters for other empirical datasets. Using Reads2Map, users can select the pipeline and parameters that best fit their data context. The Reads2MapApp shiny app provides a graphical representation of the results to facilitate their interpretation.

Angular leaf spot (ALS) is a disease that causes major yield losses in the common bean crop. Studies based on different isolates and populations have already been carried out to elucidate the genetic mechanisms of resistance to ALS. However, understanding of the interaction of this resistance with the reproductive stages of common bean is lacking. The aim of the present study was to identify ALS resistancelociat different plant growth stages (PGS) by association and linkage mapping approaches. An BC₂F₃inter-gene pool cross population (AND 277 × IAC-Milênio – AM population) profiled with 1,091 SNPs from genotyping by sequencing (GBS) was used for linkage mapping, and a carioca diversity panel (CDP) genotyped by 5,398 SNPs from BeadChip assay technology was used for association mapping. Both populations were evaluated for ALS resistance at the V2 and V3 PGSs (controlled conditions) and R8 PGS (field conditions). Different QTL (quantitative traitloci) were detected for the three PGSs and both populations, showing a different quantitative profile of the disease at different plant growth stages. For the three PGS, multiple interval mapping (MIM) identified seven significant QTL, and the Genome-wide association study (GWAS) identified fourteen associate SNPs. Severallocivalidated regions of previous studies, andPhg-1,Phg-2, Phg-4, andPhg-5, among the 5lociof greatest effects reported in the literature, were detected in the CDP. The AND 277 cultivar contained both thePhg-1and thePhg-5QTL, which is reported for the first time in the descendant cultivar CAL143 as ALS10.1^UC. The novel QTL named ALS11.1^AMwas located at the beginning of chromosome Pv11. Gene annotation revealed several putative resistance genes involved in the ALS response at the three PGSs, and with the markers andlociidentified, new specific molecular markers can be developed, representing a powerful tool for common bean crop improvement and for gain in ALS resistance.

Accurate QTL mapping in outcrossing species requires software programs which consider genetic features of these populations, such as markers with different segregation patterns and different level of information. Although the available mapping procedures to date allow inferring QTL position and effects, they are mostly not based on multilocus genetic maps. Having a QTL analysis based in such maps is crucial since they allow informative markers to propagate their information to less informative intervals of the map. We developed fullsibQTL , a novel and freely available R package to perform composite interval QTL mapping considering outcrossing populations and markers with different segregation patterns. It allows to estimate QTL position, effects, segregation patterns, and linkage phase with flanking markers. Additionally, several statistical and graphical tools are implemented, for straightforward analysis and interpretations. fullsibQTL is an R open source package with C and R source code (GPLv3). It is multiplatform and can be installed from https://github.com/augusto-garcia/fullsibQTL .

Rose rosette disease (RRD), caused by the rose rosette emaravirus (RRV), is a major viral disease in roses (Rosa sp.) that threatens the rose industry. Recent studies have revealed quantitative trait loci (QTL) for reduced susceptibility to RRD in the linkage groups (LGs) 1, 5, 6, and 7 in tetraploid populations and the LGs 1, 3, 5, and 6 in diploid populations. In this study, we seek to better localize and understand the relationship between QTL identified in both diploid and tetraploid populations. We do so by remapping the populations found in these studies and performing a meta-analysis. This analysis reveals that the peaks and intervals for QTL using diploid and tetraploid populations co-localized on LG 1, suggesting that these are the same QTL. The same was seen on LG 3. Three meta-QTL were identified on LG 5, and two were discovered on LG 6. The meta-QTL on LG 1, MetaRRD1.1, had a confidence interval (CI) of 10.53 cM. On LG 3, MetaRRD3.1 had a CI of 5.94 cM. MetaRRD5.1 had a CI of 17.37 cM, MetaRRD5.2 had a CI of 4.33 cM, and MetaRRD5.3 had a CI of 21.95 cM. For LG 6, MetaRRD6.1 and MetaRRD6.2 had CIs of 9.81 and 8.81 cM, respectively. The analysis also led to the identification of potential disease resistance genes, with a primary interest in genes localized in meta-QTL intervals on LG 5 as this LG was found to explain the greatest proportion of phenotypic variance for RRD resistance. The results from this study may be used in the design of more robust marker-based selection tools to track and use a given QTL in a plant breeding context.