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Fine mapping QTLs using Recombinant-Inbred HS and In-Vitro HS

Fine mapping QTLs using Recombinant-Inbred HS and In-Vitro HS. William Valdar Jonathan Flint, Richard Mott Wellcome Trust Centre for Human Genetics. Heterogeneous Stocks. 8 inbred lines. Pseudo-random mating for N generations. typical chromosome pair. eg, N=30: 3.4cM (=100/30)

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Fine mapping QTLs using Recombinant-Inbred HS and In-Vitro HS

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  1. Fine mapping QTLs using Recombinant-Inbred HS and In-Vitro HS William Valdar Jonathan Flint, Richard Mott Wellcome Trust Centre for Human Genetics

  2. Heterogeneous Stocks 8 inbred lines Pseudo-random mating for N generations typical chromosome pair eg, N=30: 3.4cM (=100/30) average distance between recombinants

  3. Cost of mapping with HS • Need to genotype markers at very high density (sub centimorgan) • Expensive to genotype whole genome (eg 3000 markers for 30 generation HS) • How can we reduce genotyping cost ? • Use multiple phenotypes (value for money) Two genetic strategies: • RIHS Recombinant Inbred Heterogeneous Stock • IVHS In vitro Heterogeneous Stock

  4. Recombinant Inbred HS (RIHS) X 20 generations HS HS RIHS

  5. Recombinant Inbred HS (RIHS) • Genotype each RIHS line once • Keep stock, eg, as embryos • Distribute RIHS lines to labs for phenotyping X 20 generations HS HS RIHS

  6. Recombinant Inbred HS (RIHS) • Genotype each RIHS line once • Keep stock, eg, as embryos • Distribute RIHS lines to labs for phenotyping X 20 generations HS HS RIHS Advantage over standard RI : resolution Advantage over standard HS: cost

  7. RIHS for mapping modifier QTL X X 20 generations inbred HS HS RIHS F1 (may contain knockout or transgene) modifier search

  8. How many RIHS do we need for effective fine-mapping? • Are there other HS strategies to reduce genotyping…?

  9. In Vitro HS (IVHS) meiosis Fertilize inbred dam with HS sperm IVF recombinant F1 HS sperm HS donor

  10. IVHS-1 meiosis IVF recombinant genotype donors at high resolution F1 HS sperm HS donor

  11. IVHS-1 meiosis IVF recombinant pass 1 pass 2 genotype donors at high resolution F1 HS sperm HS donor F1 markers

  12. IVHS-2 meiosis IVF no further genotyping recombinant genotype donors at high resolution F1 HS sperm HS donor treat as average of donor chromosomes

  13. Simulations • Compare strategies RIHS, IVHS-1, IVHS-2 by simulation

  14. Simulations • Compare strategies RIHS, IVHS-1, IVHS-2 by simulation • Simulate 25cM chromosome with single additive QTL placed randomly

  15. Simulations • Compare strategies RIHS, IVHS-1, IVHS-2 by simulation • Simulate 25cM chromosome with single additive QTL placed randomly • Type 100 SNP markers

  16. Simulations • Compare strategies RIHS, IVHS-1, IVHS-2 by simulation • Simulate 25cM chromosome with single additive QTL placed randomly • Type 100 SNP markers • 30 generation HS

  17. Simulations • Compare strategies RIHS, IVHS-1, IVHS-2 by simulation • Simulate 25cM chromosome with single additive QTL placed randomly • Type 100 SNP markers • 30 generation HS • Vary • QTL effect size (1% to 50%) • # RIHS lines used (40, 80, 120) • Sample size (400 to 2000 total number of pups)

  18. Simulations • Compare strategies RIHS, IVHS-1, IVHS-2 by simulation • Simulate 25cM chromosome with single additive QTL placed randomly • Type 100 SNP markers • 30 generation HS • Vary • QTL effect size (1% to 50%) • # RIHS lines used (40, 80, 120) • Sample size (400 to 2000 total number of pups) • Also investigate for IVHS-1 • Marker density • SNPs v Microsatellites • # HS generations

  19. Evaluating the simulations • Evaluation • Perform 1000 simulations per condition • Analysis performed with HAPPY • Probability of detecting a QTL (must be a marker interval with adjusted HAPPY Pvalue < 1%) • Mapping accuracy

  20. Detecting a significant locus • Pass rate = % times most significant marker interval has (corrected) P-value less than 0.01

  21. consistent across population sizes 5% Detecting a significant locus • Pass rate = % times most significant marker interval has a corrected P-value less than 0.01

  22. Mapping accuracy for significant loci • Mean mapping error = average distance between true QTL and the predicted locus mapping error (cM) predicted QTL true QTL

  23. Mapping accuracy for significant loci • Mean mapping error = average distance between true QTL and the predicted locus mapping error (cM) predicted QTL true QTL

  24. Varying marker density and marker type • IVHS-1 strategy with 5%QTL, 1200 pups • Vary number of markers over a 3cM region

  25. Microsats = SNPs Microsats better ~0.05cM Varying marker density and marker type • IVHS-1 strategy with 5%QTL, 1200 pups • Vary number of markers over a 3cM region

  26. Varying number of HS generations • IVHS-1 strategy with 5%QTL, 1200 pups

  27. Varying number of HS generations • IVHS-1 strategy with 5%QTL, 1200 pups optimum [5,15]

  28. Conclusions • RIHS and IVHS strategies: low genotyping cost without sacrificing mapping resolution • IVHS is short term mapping strategy • RIHS takes longer, costs more but is long term strategy of choice. • 100 RIHS lines is sufficient for mapping isolated additive QTLs but may not be enough for • multiple QTLs • identifying epistatic effects • Suitable HS: need only 15 generations Paper submitted to Mammalian Genome (preprints available)

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