Introduction | Summary of available functions | Considerations for genome biology | Estimating genotype probabilities in a mapping population | Data import example with UNEAK | Quality control and genotype calling | Examining the output | Results cleanup and export | Estimating genotype probabilities in a diversity panel | Data import example with VCF | Quality control and parameter estimation | Genotype calling | Examining inheritance mode | Other genotype calling functions | $H_{ind}/H_E$ for filtering markers and individuals | Considerations for RAM and processing time | Citing polyRAD

Abstract | Fit multidog() | multidog() Output | References

Reading data set | Data quality control | Two-point analysis | Grouping | Ordering markers | Phasing markers and estimating multilocus recombination fractions. | Homolog probability and preferential pairing profile | Exporting a phased map | Utility functions | Plot genome vs. map | Map summary

Table of Contents | 1. A (very) short introduction to R and the polymapR package. | 2. Install polymapR | 3. Logging function calls and output | 4. Data importing | 4.1 Reading in marker dosage data | But first, a note on polyploid terminology | 4.2 Checking for skewness | 4.3 Running a PCA | 5. Generate summary data | 6. Convert marker dosages to simple segregations; remove non-segregating data | 7. Quality checks on marker data | 7.1 Missing value rate per marker | 7.2 Missing value rate per individual | 7.3 Duplicate individuals | 7.4 Duplicated markers | 8. Simplex x nulliplex markers – defining chromosomes and homologues | A note on arguments and multi-core processing | A note on nested lists | 8.1 Using the function overviewSNlinks | 8.2 Using the function cluster_per_LG | 8.3 Cross-ploidy populations (e.g. tetraploid x diploid to give triploid F1) | 9. Assigning SxS and DxN markers and consensus linkage group (LG) names | 10. Assign all other markertypes | 11. Finish the linkage analysis | 12. Marker ordering | 12.1 Creating an integrated chromosomal linkage map | 12.2 Optional: Adding back duplicated markers | 12.3 Phasing an integrated map | 13. Plotting a map | 14. Evaluating map quality | 15. Preferential pairing | 16. QTL analysis | 17. Concluding remarks | 18. References

Exercise 1: Segregation types and quality | Exercise 2: checking for "shifted" markers | Exercise 3: select acceptably scored markers | Exercise 4: combine probes

Exercise 1: extract data from SNP array | Illumina | Affymetrix | Read and convert the array data | Exercise 2: Rename the samples and separate by ploidy level | Exercise 3: Filtering the data based on total signal intensity | 3a: Selection of samples based on total signal intensity | 3b: Selection of markers based on total signal intensity | 3c: Selection based on R values of samples within markers | Excersise 4: run fitPoly

Introduction | Installing polyqtlR | IBD probabilities | Data structures | HMM for IBD estimation | Optional: Importing IBDs from TetraOrigin or PolyOrigin | TetraOrigin | PolyOrigin | "Heuristic" method for IBD estimation | High marker densities | Interpolating IBDs | Visualising IBD haplotypes | Genotypic Information | Performing a QTL scan | Including experimental blocks | Running an initial QTL scan | Visualising QTL results | Significance thresholds | BLUEs | Adding genetic co-factors | Manual co-factor analysis | Automatic co-factor analysis | Comparing multiple analyses | Exploring the QTL peak | Compare QTL results with offspring | Single Marker Analysis | PVE | Meiosis & Recombinations | Meiosis report | Preferential pairing | Recombination landscape | Searching for recombinant individuals | Fishing for specific alleles | Correcting genotyping errors | Concluding remarks | References

Table of Contents | 1. Introduction | 2. Importing sample data | 3. Initial mapping steps | 4. Data filtering | 4.1. Quality check on offspring scores | 4.2. Distribution of maximum genotype probabilities | 4.3. Screen for duplicate individuals | 4.4. Screen for duplicate markers | 4.5. Screening for missingness | 5. Marker conversion | 6. Linkage analysis | 6.1 Simplex markers - clustering homologues | 6.2 Higher-dose markers - connecting clustered homologues | 6.3 Marker assignments | 7. Ordering the map | 8. References

When and why to use this pipeline | Pipeline overview | TASSEL-GBSv2 | If TASSEL-GBS won't work for your dataset | Alignment with Bowtie2 | Grouping tags by alignment sets | Filtering samples and estimating inbreeding | Sorting tags into isoloci and filtering isoloci | Genotype calling

Structure of the input files | 1) Pedigree file | 2) Genotype file | 3) Phenotype file | Read the data | Setting parameters | QTL Discovery | Dominance | QTL Modeling | Haplotype-based Selection

Roeland E. Voorrips, r Sys.Date() | Input data | Haploblock definitions | Marker data | Pedigree | FS families | Haplotyping | Overviews and statistics | Overviews by FS family | Pedigree check | Summary statistics | Number of markers vs number of haplotypes | Segregation in one FS, one haploblock | Remarks

What's Updog? | Example from an S1 Population | Fit updog | Analyze Output | Filtering SNPs | References

Abstract | Controlling Misclassification Error | Visualizing the Joint Distribution | References

Abstract | Analysis | F1 Population | HWE Population | References