Smooth Descent: A ploidy-aware algorithm to improve linkage mapping in the presence of genotyping errors

Navarro, Alejandro Thérèse; Bourke, Peter M.; Weg, Eric van de; Clot, Corentin R.; Arens, Paul; Finkers, Richard; Maliepaard, Chris

doi:10.3389/fgene.2023.1049988

Cited by 4 publications

(6 citation statements)

References 49 publications

(71 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, Bilton et al (2018) developed a method based on the Lander-Green hidden Markov model and implemented in the software GUSMap to account for sequencing errors in GBS data and reduce upward bias in genetic map lengths in outbred populations, especially when read depth is low. Navarro et al (2023) developed an iterative algorithm, implemented in the software Smooth Descent, to detect errors in low-depth GBS data and impute the most probable alternative genotypes, which resulted in genetic maps with improved marker order and map lengths. Smooth descent requires accurate parental phasing, which would necessitate high-depth long-read sequencing of the parents; if marker order can be corrected using a reference genome, error correction of markers can still be performed with this software.…”

Section: Map-construction Methods Depthmentioning

confidence: 99%

Improving precision and accuracy of genetic mapping with genotyping‐by‐sequencing data in outcrossing species

LaBonte,

Zerpa‐Catanho,

Liu

et al. 2024

GCB Bioenergy

View full text Add to dashboard Cite

Genotyping‐by‐sequencing (GBS) is a widely used strategy for obtaining large numbers of genetic markers in model and non‐model organisms. In crop plants, GBS‐derived marker datasets are frequently used to perform quantitative trait locus (QTL) mapping. In some plant species, however, high heterozygosity and complex genome structure mean that researchers must use care in handling GBS data to conduct QTL mapping most effectively. Such outbred crops include most of the perennial grass and tree species used for bioenergy. To identify strategies for increasing accuracy and precision of QTL mapping using GBS data in outbred crops, we conducted an empirical study of SNP‐calling and genetic map‐building pipeline parameters in a Miscanthus sinensis population, and a complementary simulation study to estimate the relationship between genome‐wide error rate, read depth, and marker number. The bioenergy grass Miscanthus is an obligate outcrossing species with a recent (diploidized) whole‐genome duplication. For the study of empirical M. sinensis data, we compared two SNP‐calling methods (one non‐reference‐based and one reference‐based), a series of depth filters (12×, 20×, 30×, and 40×) and two map‐construction methods (i.e., marker ordering: linkage‐only and order‐corrected based on a reference genome). We found that correcting the order of markers on a linkage map by using a high‐quality reference genome improved QTL precision (shorter confidence intervals). For typical GBS datasets of between 1000 and 5000 markers to build a genetic map for biparental populations, a depth filter set at 30× to 40× applied to outbred populations provided a genome‐wide genotype‐calling error rate of less than 1%, improved accuracy of QTL point estimates and minimized type I errors for identifying QTL. Based on these results, we recommend using a reference genome to correct the marker order of genetic maps and a robust genotype depth filter to improve QTL mapping for outbred crops.

show abstract

Section: Map-construction Methods Depthmentioning

confidence: 99%

Improving precision and accuracy of genetic mapping with genotyping‐by‐sequencing data in outcrossing species

LaBonte,

Zerpa‐Catanho,

Liu

et al. 2024

GCB Bioenergy

View full text Add to dashboard Cite

show abstract

“…ConGenR rapidly determines consensus genotypes and estimates genotyping errors from replicated genetic samples [ 27 ]. Smooth and Smooth-Descent predict genotyping errors, which improve the map quality and correctness of marker sequence [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Effect of genotyping errors on linkage map construction based on repeated chip analysis of two recombinant inbred line populations in wheat (Triticum aestivum L.)

Wang,

Xia

et al. 2024

BMC Plant Biol

View full text Add to dashboard Cite

Linkage maps are essential for genetic mapping of phenotypic traits, gene map-based cloning, and marker-assisted selection in breeding applications. Construction of a high-quality saturated map requires high-quality genotypic data on a large number of molecular markers. Errors in genotyping cannot be completely avoided, no matter what platform is used. When genotyping error reaches a threshold level, it will seriously affect the accuracy of the constructed map and the reliability of consequent genetic studies. In this study, repeated genotyping of two recombinant inbred line (RIL) populations derived from crosses Yangxiaomai × Zhongyou 9507 and Jingshuang 16 × Bainong 64 was used to investigate the effect of genotyping errors on linkage map construction. Inconsistent data points between the two replications were regarded as genotyping errors, which were classified into three types. Genotyping errors were treated as missing values, and therefore the non-erroneous data set was generated. Firstly, linkage maps were constructed using the two replicates as well as the non-erroneous data set. Secondly, error correction methods implemented in software packages QTL IciMapping (EC) and Genotype-Corrector (GC) were applied to the two replicates. Linkage maps were therefore constructed based on the corrected genotypes and then compared with those from the non-erroneous data set. Simulation study was performed by considering different levels of genotyping errors to investigate the impact of errors and the accuracy of error correction methods. Results indicated that map length and marker order differed among the two replicates and the non-erroneous data sets in both RIL populations. For both actual and simulated populations, map length was expanded as the increase in error rate, and the correlation coefficient between linkage and physical maps became lower. Map quality can be improved by repeated genotyping and error correction algorithm. When it is impossible to genotype the whole mapping population repeatedly, 30% would be recommended in repeated genotyping. The EC method had a much lower false positive rate than did the GC method under different error rates. This study systematically expounded the impact of genotyping errors on linkage analysis, providing potential guidelines for improving the accuracy of linkage maps in the presence of genotyping errors.

show abstract

“…Phased bin-markers were visualized, and phasing was manually improved, when necessary, with the function manualPhasing before being formatted for Smooth Descent (Thérèse Navarro et al, 2023) with the function formatSD.…”

Section: Clustering Of Adjacent Sequence Variants Into Robust Marker ...mentioning

confidence: 99%

“…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 (Thérèse Navarro et al, 2023). Briefly, 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 flanking markers.…”

Section: Correcting Genotyping Errors With Smooth Descentmentioning

confidence: 99%

See 1 more Smart Citation

Natural variation in potato sexual reproduction facilitates breeding

Clot

View full text Add to dashboard Cite

The origin and widespread occurrence of Sli based self-compatibility in potato Chapter 3 46 High-density linkage map constructed from a skim sequenced diploid potato population reveals transmission distortion and QTLs for tuber yield and pollen shed Chapter 4 74Desynapsis in potato is caused by StMSH4 mutant alleles and leads to either highly uniform unreduced pollen or sterility Chapter 5 94Identification of two mutant JASON-RELATED genes associated with unreduced pollen production in potato Chapter 6 122 General discussion References 143Summary 167Acknowledgements 171 About the author 179Education statement recognition model (Kubo et al., 2010), the combined action of the SLFs encoded by one S-haplotype results in the recognition and degradation of all S-RNases except of the one encoded by their own haplotype. Hence, self-incompatibility occurs when the Shaplotype of the pollen matches either of the two S-haplotypes of the style. This elegant model also provides an explanation for the phenomenon of competitive interaction that allows tetraploid potatoes to become self-compatible (Olsder and Hermsen, 1976). When tetraploid clones produce diploid heteroallelic pollen that expresses two complementary sets of S-locus F-box proteins (SLFs), all S-RNases are ubiquitinated, which breaks down gametophytic self-incompatibility.Recently, self-compatible diploid potato clones were engineered by a knock-out of the gene encoding for the S-RNase (Enciso-Rodriguez et al., 2019;Ye et al., 2018). Furthermore, a self-compatible diploid clone bearing a naturally weakly expressed S-RNase was recently identified . Other cases of self-compatibility among S. tuberosum dihaploids were reported in earlier studies (De Jong and Rowe, 1971;Olsder and Hermsen, 1976), but the genetic and mechanistic basis of their selfcompatibility remained undetermined prior to this thesis. Despite those alternative options, the most widely used source of self-compatibility is the dominant gene Sli originally identified from a S. chacoense accession and located on chromosome 12 Hanneman, 1998a, 1998b). The causal gene behind Sli was recently shown to encode for a pollen-expressed F-box protein functioning as a general S-RNase inhibitor (Eggers et al., 2021;Ma et al., 2021).

show abstract

Smooth Descent: A ploidy-aware algorithm to improve linkage mapping in the presence of genotyping errors

Cited by 4 publications

References 49 publications

Improving precision and accuracy of genetic mapping with genotyping‐by‐sequencing data in outcrossing species

Improving precision and accuracy of genetic mapping with genotyping‐by‐sequencing data in outcrossing species

Effect of genotyping errors on linkage map construction based on repeated chip analysis of two recombinant inbred line populations in wheat (Triticum aestivum L.)

Natural variation in potato sexual reproduction facilitates breeding

Contact Info

Product

Resources

About