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.

Background/Objectives: Brazil has a highly admixed population. Polygenic risk scores (PRSs) have mostly been developed from European population studies, and their application to other populations is challenging. To assess the use of PRS for breast cancer (BC) risk in Brazil, we evaluated four PRSs in the Brazilian population. Methods: We analyzed a Brazilian cohort composed of 6206 women with a history of breast cancer and 8878 unphenotyped adults as controls. Genomic variants were imputed from exomes, and scores were calculated for all samples. Results: After individuals with known pathogenic or likely pathogenic variants in BRCA1, BRCA2, PALB2, PTEN, or TP53 genes, and first-degree relatives of the probands were excluded, 5598 cases and 8767 controls remained. Four PRS models were compared, and PRS₃₈₂₀ achieved the best performance, with an odds ratio (OR) of 1.43 per standard deviation increase (p value < 0.001) and an OR of 1.88 (p value < 0.001) for the top decile. PRS₃₈₂₀ also performed well for different ancestry groups: East Asian majority (OR 1.59, p value 0.004), Non-European majority (OR 1.45, p value < 0.001), and European majority (OR 1.43, p value < 0.001). Conclusions: Among the different PRSs, PRS₃₁₃ and PRS₃₈₂₀ could be validated in our Brazilian cohort, with the latter exhibiting the best performance. While further clinical studies are necessary to guide clinical practice, this work represents an important step toward improving BC precision medicine in Brazil.

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 PCR 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 SNP calling and updog, polyRAD, and SuperMASSA for genotype calling, and 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 required of 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.

Sporisorium scitamineum is the causal agent of sugarcane smut, affecting global sugarcane production. Despite advances in smut genomics, the relationships between fungal genetic diversity, host adaptation, and virulence remain elusive. We applied chromosome-level genome sequencing (Oxford Nanopore and Illumina technologies) of two haploid strains (MAT-1 x MAT-2) per isolate and high-depth transcriptomic profiling (Illumina) during early infection (48 hpi) of a more virulent isolate (SSC04) in resistant (SP80-3280) and susceptible (IAC66-6) sugarcane genotypes. Despite the overall genomic similarity (99.9%), we identified nine highly polymorphic genomic islands (HPIs). The most variant HPI harbors the mating-type loci, where dense sequence variation, intrachromosomal rearrangements, and inversions, potentially linked to transposable element remnants, were observed. Additionally, the genome-wide analysis revealed non-synonymous single-nucleotide variants (SNVs) in 160 genes, including those involved in vesicular trafficking and candidate-secreted effectors. Transcriptomic profiling of the more virulent isolated revealed host-dependent transcriptional reprogramming in response to immune and metabolic cues, driving distinct infection strategies: in resistant plants, the fungus upregulated genes associated with detoxification, nitrogen starvation responses, and cell wall-degrading enzymes, while in susceptible hosts, it induced genes related to hyphal growth, lipid catabolism, and the unfolded protein response. The repertoire of expressed candidate effector genes also varied according to host and isolate genotypes. These findings uncover genomic signatures and context-dependent transcriptional regulation shaping the adaptive landscape of S. scitamineum virulence, identifying targets for pathogen monitoring and breeding for resistance.

In 2015, we published the complete genome assembly of Sporisorium scitamineum , the fungal pathogen responsible for sugarcane smut disease, generated from PacBio long-read sequencing and polished with Illumina short reads. Since then, the tools for genome assembly have improved considerably, including advances in sequencing technologies and bioinformatics workflows, motivating us to revisit our original assembly. Here, we present an updated genome version, using newly generated high-quality Illumina short-read data and DeepVariant-based polishing, resulting in corrections at approximately 5,500 genomic positions. BUSCO benchmarking in protein mode demonstrated increased genome completeness from the original 94.2% to 99.3%, indicating substantial improvements in both accuracy and gene annotation reliability. Additionally, comparison with publicly available assemblies reveals that most sequence polymorphisms occur in isolates belonging to the Asian lineage, consistent with earlier population-level studies. This enhanced genome assembly provides a significantly improved reference resource, enabling more robust genetic, evolutionary, and functional studies of this economically important sugarcane pathogen.