Using probabilistic genotypes in linkage analysis of polyploids

Abstract

Key message In polyploids, linkage mapping is carried out using genotyping with discrete dosage scores. Here, we use probabilistic genotypes and we validate it for the construction of polyploid linkage maps. Abstract Marker genotypes are generally called as discrete values: homozygous versus heterozygous in the case of diploids, or an integer allele dosage in the case of polyploids. Software for linkage map construction and/or QTL analysis usually relies on such discrete genotypes. However, it may not always be possible, or desirable, to assign definite values to genotype observations in the presence of uncertainty in the genotype calling. Here, we present an approach that uses probabilistic marker dosages for linkage map construction in polyploids. We compare our method to an approach based on discrete dosages, using simulated SNP array and sequence reads data with varying levels of data quality. We validate our approach using experimental data from a potato (Solanum tuberosum L.) SNP array applied to an F1 mapping population. In comparison to the approach based on discrete dosages, we mapped an additional 562 markers. All but three of these were mapped to the expected chromosome and marker position. For the remaining three markers, no physical position was known. The use of dosage probabilities is of particular relevance for map construction in polyploids using sequencing data, as these often result in a higher level of uncertainty regarding allele dosage.

Advances in genomic characterization ofUrochloa humidicola: exploring polyploid inheritance and apomixis

“…Several statistical tools for marker dosage estimation have been developed, most assigning genotypic classes as discrete dosages (Blischak et al 2018). Fortunately, some downstream applications can accommodate uncertainty in genotype calls (probabilistic genotypes), increasing the information available for each polyploid SNP locus (Gerard et al 2018; Liao et al 2021). The SuperMASSA tool (Serang et al 2012) is one of these applications, and we used it to accurately assess the occurrence of all possible SNP dosages in U. humidicola for the first time.…”

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

“…Here, significant SNPs detected with GEMMA software encompassed all the SNPs detected with the additive models implemented in the GWASpoly package, implying the superiority of continuous genotype call in our dataset. It has been repeatedly highlighted that discrete genotype calling may introduce errors in the data, resulting in the loss of information, and in polyploid species, it becomes increasingly difficult to assign exact discrete genotype classes, especially in higher ploidy and with sequencing data at lower read depth (Tumino et al, 2016;Yamamoto et al, 2020;Liao et al, 2021). Grandke et al (2016) conducted an association study using regression methods and concluded that continuous genotype was superior to discrete genotypes.…”

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

“…Combining information from both parental genotypes defines the expected Mendelian segregation for each locus. The informative combinations for outcrossing species with biallelic codominant markers must have at least one heterozygous genotype in one of the parents, including the marker types B3.7, D1.10, and D2.15 (Supplementary figures [13][14][15][16]. The haplotype-based multiallelic codominant markers can also present types A.1, A.2, D1.9, and D2.14.…”

“…Recently developed approaches to build linkage maps (Bilton et al, 2018; Mollinari and Garcia, 2019; Liao et al, 2021) were implemented in (Margarido et al, 2007) 3.0 package. They use quantitative genotype probability measurements rather than the traditional qualitative genotypic information from SNP and genotype calling methods to account for genotyping errors and provide higher-quality genetic maps.…”