Heterozygous Mapping Strategy (HetMappS) for High Resolution Genotyping-By-Sequencing Markers: A Case Study in Grapevine

Abstract

Genotyping by sequencing (GBS) provides opportunities to generate high-resolution genetic maps at a low genotyping cost, but for highly heterozygous species, missing data and heterozygote undercalling complicate the creation of GBS genetic maps. To overcome these issues, we developed a publicly available, modular approach called HetMappS, which functions independently of parental genotypes and corrects for genotyping errors associated with heterozygosity. For linkage group formation, HetMappS includes both a reference-guided synteny pipeline and a reference-independent de novo pipeline. The de novo pipeline can be utilized for under-characterized or high diversity families that lack an appropriate reference. We applied both HetMappS pipelines in five half-sib F1 families involving genetically diverse Vitis spp. Starting with at least 116,466 putative SNPs per family, the HetMappS pipelines identified 10,440 to 17,267 phased pseudo-testcross (Pt) markers and generated high-confidence maps. Pt marker density exceeded crossover resolution in all cases; up to 5,560 non-redundant markers were used to generate parental maps ranging from 1,047 cM to 1,696 cM. The number of markers used was strongly correlated with family size in both de novo and synteny maps (r = 0.92 and 0.91, respectively). Comparisons between allele and tag frequencies suggested that many markers were in tandem repeats and mapped as single loci, while markers in regions of more than two repeats were removed during map curation. Both pipelines generated similar genetic maps, and genetic order was strongly correlated with the reference genome physical order in all cases. Independently created genetic maps from shared parents exhibited nearly identical results. Flower sex was mapped in three families and correctly localized to the known sex locus in all cases. The HetMappS pipeline could have wide application for genetic mapping in highly heterozygous species, and its modularity provides opportunities to adapt portions of the pipeline to other family types, genotyping technologies or applications.

Construction of a dense SNP map of a highly heterozygous diploid potato population and QTL analysis of tuber shape and eye depth

“…While we replicated within individual time points since each phenotype was the average of eight leaf Table 1 Statistics for the QTL found from the single phenotype stepwise regression analysis performed on breeding values of the V. rupestris B38 x 'Horizon' (RH) and 'Horizon' × V. cinerea B9 (HC) F 1 families incorporating data across 2 years and multiple experiments a The heterozygous parent is the one that has the heterozygous allele. SNPs for the genotypes in the families either were homozygous for one allele or heterozygous because only pseudo-testcross markers were used to build the genetic maps (Hyma et al 2015) b Calculated using 1000 permutation tests with an alpha value of 0.05 c Given for the most significant marker in a QTL credible interval d Calculated as Type III SS Total SS × 100 for the most significant marker in a QTL credible interval. Type III sum of squares (SS) of a QTL is the SS of that QTL conditional on all other QTL in the model e The absolute value of the effect of the most significant marker in a QTL credible interval when the other QTL listed for a particular phenotype within a family are included in the model.…”

“…The SNP confidence intervals are given in Table 2 Table 2 The physical locations of QTL found using the multiple phenotype Bayesian network analysis performed on breeding values of the V. rupestris B38 × 'Horizon' (RH) and 'Horizon' × V. cinerea B9 (HC) F 1 families incorporating data across 2 years and multiple experiments a The heterozygous parent is the one that has the heterozygous allele. SNPs for the genotypes in the families either were homozygous for one allele or heterozygous because only pseudo-testcross markers were used to build the genetic maps (Hyma et al 2015) b Location intervals are based on the 12X.2 version (URGI 2014) of the grapevine reference genome. The middle value represents the location of the markers in the networks in Figs.…”

“…The genotyping error rate used to calculate conditional genotype probabilities was set to 0.001. RH and HC family genetic maps used for the analyses were made with HetMappS and were previously published (Hyma et al 2015). Imputation of missing single nucleotide polymorphism (SNP) data was performed using the expectation-maximization algorithm in rrBLUP (Endelman 2011).…”

The genetic basis of sex determination in grapevines (Vitis spp.)

“…Efforts made to understand the sex locus in grape converged on a ~150 kbp region on chromosome 2 (Dalbó et al, 2000;Riaz et al, 2006;Marguerit et al, 2009;Fechter et al, 2012;Battilana et al, 2013;Picq et al, 2014;Hyma et al, 2015;Zhou et al, 2017) and several models that explain grapevine sex inheritance have been proposed (Valleau, 1916;Oberle, 1938;Levadoux, 1946;Doazan and Rives, 1967;Antcliff, 1980;Carbonneau, 1983). In one model, males occur when a dominant allele inhibiting ovule development is present and females occur given a recessive allele associated with the inhibition of pollen development (Oberle, 1938).…”

Design and validation of an STR hexaplex assay for DNA profiling of grapevine cultivars

“…DNA profiling based on microsatellites (STR or simple sequence repeats, SSR) is the preferred way of identifying the cultivar of Vitis vinifera L. samples because the data generated are objective, more precise than those obtained using traditional ampelographic techniques , and less expensive than analysis using SNP arrays or genotyping by next‐generation sequencing . It also has advantages over profiling of grapevine must proteins , anthocyanines , amino acids , aromatic compounds , and chemical elements because the microsatellite profile is not affected by environmental factors such as a soil composition, weather conditions, or vinification .…”