Smooth Descent: a Ploidy-Aware Algorithm To Improve Linkage Mapping In The Presence of Genotyping Errors

Abstract

Linkage mapping is an approach to order markers based on recombination events. Mapping algorithms cannot easily handle genotyping errors, which are common in high-throughput genotyping data. To solve this issue, strategies have been developed, aimed mostly at identifying and eliminating these errors. One such strategy is SMOOTH (van Os et al. 2005), an iterative algorithm to detect genotyping errors. Unlike other approaches, SMOOTH can also be used to impute the most probable alternative genotypes, but its application is limited to diploid species and to markers heterozygous in only one of the parents. In this study we adapted SMOOTH to expand its use to any marker type and to autopolyploids with the use of identity-by-descent probabilities, naming the updated algorithm Smooth Descent (SD). We applied SD to real and simulated data, showing that in the presence of genotyping errors this method produces better genetic maps in terms of marker order and map length. SD is particularly useful for error rates between 5% and 20% and when error rates are not homogeneous among markers or individuals. Moreover, the simplicity of the algorithm allows thousands of markers to be efficiently processed, thus being particularly useful for error detection in high-density datasets. We have implemented this algorithm in the R package SmoothDescent.

High-density linkage map constructed from a skim sequenced diploid potato population reveals transmission distortion and QTLs for tuber and pollen production

“…Bin-markers data with r > 0.5 were swapped to phase adjacent bin-markers with the function phaseHap. Phased bin-markers were visualized, and phasing was manually improved, when necessary, with the function manualPhasing before being formatted for Smooth Descent (Navarro et al, 2022) with the function formatSD.…”

“…The segregation data of bin-markers obtained with OutcrossSeqDiploidR were corrected for putatively erroneous data points with the algorithm Smooth Decent following the package vignette (Navarro et al, 2022). Brie y, Smooth Decent makes use of identity-by-descent probabilities, in our case based on the physical order of bin-markers, to detect putative genotyping errors and impute the most probable genotype given the data points of anking markers.…”