Smooth Descent: A Ploidy-Aware Algorithm for Improved Linkage Mapping
Smooth Descent
A ploidy-aware algorithm to improve linkage
mapping in the presence of genotyping errors
Alejandro Thérèse Navarro
December 2021
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.
Introduction
Genetic mapping in strawberry using
genotypes with high error rate
Smooth Descent: naïve IBD
Identity by Descent (IBD) probability matrix
AC
TT
Given a genetic map (marker order and distances)
Smooth Descent: prediction
1.
Prediction:
local, weighted
average of
informative
probabilities.
2.
Error
detection:
difference
between
observed and
predicted IBDs
above threshold
Smooth Descent: prediction
1.
Prediction:
local, weighted
average of
informative
probabilities.
2.
Error
detection:
difference
between
observed and
predicted IBDs
above threshold
Smooth Descent: prediction
Smooth Descent: iterative mapping
Smooth Descent is
applied
iteratively
:
1.
Correct genotypes
2.
Estimate linkage map
3.
Repeat
Smooth Descent used with strawberry sequence marker data.
Real data strawberry
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
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
Simulation tests in autopolyploids
Simple IBD estimation and genotype correction
Estimate error rates, recombinations
Conclusions
Smooth Descent is an iterative algorithm
for mapping in the presence of
genotyping errors.
Software
: github.com/alethere/smoothdescent
Article
: preprint Theoretical Applied Genetics
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
Acknowledgements
Thanks for your attention
Questions?
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.
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Presentation Transcript
Smooth Descent A ploidy-aware algorithm to improve linkage mapping in the presence of genotyping errors Alejandro Th r se Navarro December 2021
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.
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
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
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
Smooth Descent: prediction IBD probabilities: 1 0.5 0
Smooth Descent: iterative mapping Smooth Descent is applied iteratively: 1. Correct genotypes 2. Estimate linkage map 3. Repeat
Real data strawberry Smooth Descent used with strawberry sequence marker data.
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
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
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
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?
