Smooth Descent: A Ploidy-Aware Algorithm for Improved Linkage Mapping

Introducing Smooth Descent, an algorithm designed to enhance linkage mapping accuracy in the presence of genotyping errors. This algorithm iteratively eliminates errors to refine map order, accommodating various marker types and ploidies. By predicting and detecting errors in Identity by Descent (IBD) probabilities, Smooth Descent achieves an improved map order. Real data from strawberry markers and simulation tests with diploid populations showcase the efficacy of this approach.

• Linkage mapping
• Genotyping errors
• Smooth Descent
• Ploidy-aware algorithm
• Genetic mapping

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1. Smooth Descent A ploidy-aware algorithm to improve linkage mapping in the presence of genotyping errors Alejandro Th r se Navarro December 2021

2. Introduction Genetic mapping in strawberry using genotypes with high error rate SMOOTH (van Os et al. 2005) genotyping error detection based on map order. Eliminate genotyping errors iteratively to improve map order. Applicable 1x0 or 0x1 markers in diploids. Implementation in R and some changes: All marker types used, all (even) ploidies accepted, improved prediction method.

3. Smooth Descent: nave IBD Given a genetic map (marker order and distances) P1 P2 hom1 hom2 hom3 hom4 genotype Hom 1 Hom 2 Hom 3 Hom 4 A T A A C C C A A G T A C T G G C C C A A G C C AC 1 0 0.5 0.5 TT 1 0 1 0 AG 0.5 0.5 1 0 AG 1 0 0.5 0.5 TC 0 1 0.5 0.5 CC 1 0 0.5 0.5 A A A T C A A T AC 0.5 0.5 1 0.5 AA 1 0 1 0 Identity by Descent (IBD) probability matrix

4. Smooth Descent: prediction 1. Prediction: local, weighted average of informative probabilities. 2. Error detection: difference between observed and predicted IBDs above threshold IBD probabilities: 1 0.5 0

5. Smooth Descent: prediction 1. Prediction: local, weighted average of informative probabilities. 2. Error detection: difference between observed and predicted IBDs above threshold IBD probabilities: 1 0.5 0

6. Smooth Descent: prediction IBD probabilities: 1 0.5 0

7. Smooth Descent: iterative mapping Smooth Descent is applied iteratively: 1. Correct genotypes 2. Estimate linkage map 3. Repeat

8. Real data strawberry Smooth Descent used with strawberry sequence marker data.

9. Simulation tests Simulated 10 diploid populations 100 individuals. Added error rates to test SD efficacy. Special A: heterogeneous rate across individuals Special B: heterogeneous rate along map Optimal with few iterations

10. Simulation tests in autopolyploids Map reconstruction is also improved. Genotype correctness is also improved. Less effective as ploidy increases: 1. Genotyping errors affect order more 2. Less corrections can be made due to IBD uncertainty

11. Conclusions Smooth Descent is an iterative algorithm for mapping in the presence of genotyping errors. Simple IBD estimation and genotype correction Estimate error rates, recombinations Software: github.com/alethere/smoothdescent Article: preprint Theoretical Applied Genetics

12. Acknowledgements Peter Bourke Eric van de Weg Chris Maliepaard Fernanda Alves Freitas Guesdes Natascha van Lieshout Paul Arens Richard Finkers Herman van Eck Johan Willemsen Thijs van Dijk Thanks for your attention Questions?