Using probabilistic genotypes in linkage analysis of polyploids

Advances in genomic characterization of Urochloa humidicola: exploring polyploid inheritance and apomixis

KEY MESSAGE

We present the highest-density genetic map for the hexaploid Urochloa humidicola. SNP markers expose genetic organization, reproduction, and species origin, aiding polyploid and tropical forage research. Tropical forage grasses are an important food source for animal feeding, with Urochloa humidicola, also known as Koronivia grass, being one of the main pasture grasses for poorly drained soils in the tropics. However, genetic and genomic resources for this species are lacking due to its genomic complexity, including high heterozygosity, evidence of segmental allopolyploidy, and reproduction by apomixis. These complexities hinder the application of marker-assisted selection (MAS) in breeding programs. Here, we developed the highest-density linkage map currently available for the hexaploid tropical forage grass U. humidicola. This map was constructed using a biparental F1 population generated from a cross between the female parent H031 (CIAT 26146), the only known sexual genotype for the species, and the apomictic male parent H016 (BRS cv. Tupi). The linkage analysis included 4873 single nucleotide polymorphism (SNP) markers with allele dosage information. It allowed mapping of the ASGR locus and apospory phenotype to linkage group 3, in a region syntenic with chromosome 3 of Urochloa ruziziensis and chromosome 1 of Setaria italica. We also identified hexaploid haplotypes for all individuals, assessed the meiotic configuration, and estimated the level of preferential pairing in parents during the meiotic process, which revealed the autopolyploid origin of sexual H031 in contrast to apomictic H016, which presented allopolyploid behavior in preferential pairing analysis. These results provide new information regarding the genetic organization, mode of reproduction, and allopolyploid origin of U. humidicola, potential SNPs markers associated with apomixis for MAS and resources for research on polyploids and tropical forage grasses.

Developing best practices for genotyping-by-sequencing analysis in the construction of linkage maps

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.

Genome-Wide Identification of Loci Associated With Phenology-Related Traits and Their Adaptive Variations in a Highbush Blueberry Collection

Genetic variation in phenological traits is the key in expanding production areas of crops. Southern highbush blueberry (SHB) is a blueberry cultivar group adapted to warmer climates and has been developed by multiple interspecific hybridizations between elite northern highbush blueberry (NHB) (Vaccinium corymbosum L.) and low-chill Vaccinium species native to the southern United States. In this study, we employed a collection of diverse SHB accessions and performed a genome-wide association study (GWAS) for five phenology-related traits [chilling requirement (CR), flowering date, ripening date, fruit development period, and continuous flowering] using polyploid GWAS models. Phenology-related traits showed higher heritability and larger correlation coefficients between year replications, which resulted in the detection of robust phenotype-genotype association peaks. Notably, a single association peak for the CR was detected on Chromosome 4. Comparison of genotypes at the GWAS peaks between NHB and SHB revealed the putative introgression of low-chill and late-flowering alleles into the highbush genetic pool. Our results provide basic insights into the diversity of phenological traits in blueberry and the genetic establishment of current highbush cultivar groups.