Building highly saturated genetic maps with OneMap 3.0: new approaches using workflows - Taniguti, Cristiane Hayumi (2021)
OneMap is an R package developed by members of Statistical Genetics Laboratory at ESALQ/USP (Brazil) released in 2008. It gained the attention of the scientific community for being one of the first software for building integrated genetics maps for outcrossing species. It is now highly used worldwide. However, it requires updates to deal with the new and abundant markers generated by high-throughput genotyping techniques. In this work, we made a major update of OneMap to version 3.0, which includes: higher speed of the genetic distance estimation; new methods for group and ordering markers; new quality diagnostic graphics tools; new features for making simulations; features to the conversion of VCF file with biallelic and multiallelic to OneMap input file; possibility of include error or genotype probability to estimate the genetic distances. Once OneMap was updated, we explored the steps upstream of the map building process, which has an impact on the resulted map quality. For that, we developed the Reads2Map workflows that perform the analysis, starting with empirical or simulated sequencing reads until the final linkage maps. Because the presented workflows are written with Workflow Description Language (WDL), they provide to users a findable, accessible, interoperable, and reusable code to build maps. The workflows compare the performance of software in the linkage map building: freebayes, GATK as SNP and genotype callers; updog, polyRAD, SuperMASSA as genotype caller; OneMap 3.0 and GUSMap as linkage map builders. We also developed the shiny Reads2MapApp app to evaluate graphically the work-flow\'s results. In the particular case of an example dataset from Populus tremula, we select the freebayes as SNP and genotype caller, and a global error probability of 5%, resulting in a map with 6936 markers and 3299.961 cM. After also using the workflows, we tested the impact of two of the major OneMap 3.0 updates in the linkage maps: the usage of genotype probabilities to estimate the genetic distances and the haplotype-based multiallelic markers from assembly-based SNP caller. Using simulated sequence reads data we could measure each SNP and genotype caller efficiency and its influences in the resulted map. The impact of the genotype probabilities was variable between software according to each simulated scenario. The results showed that OneMap 3.0 can build high-quality genetic maps if i) the genotype callers do not estimate wrongly many genotypes and a global error rate of 5% is applied for all genotypes or ii) if the genotype caller estimate more genotypes wrongly it also gives lower genotype probabilities for the wrong genotypes. Furthermore, the usage of haplotype-based markers reveals to increase the order and genetic distance quality. Once the procedures upstream the genetic map building have a strong influence in its quality, the combined usage of OneMap 3.0, Reads2Map and Reads2MapApp provide to users tools to build linkage maps since the sequencing reads, and also diagnostic graphics and measures to help them to choose the best combination of software and parameters.