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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
Pseudo-random matingfor N generations
typicalchromosome
pair
8 inbred lines
eg, N=30:3.4cM (=100/30)average distance
between recombinants
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
Recombinant Inbred HS (RIHS)
X20
generations
HS HS RIHS
Recombinant Inbred HS (RIHS)
X20
generations
HS HS RIHS
• Genotype each RIHS line once
• Keep stock, eg, as embryos
• Distribute RIHS lines to labs for phenotyping
Recombinant Inbred HS (RIHS)
X20
generations
HS HS RIHS
Advantage over standard RI : resolutionAdvantage over standard HS: cost
• Genotype each RIHS line once
• Keep stock, eg, as embryos
• Distribute RIHS lines to labs for phenotyping
RIHS for mapping modifier QTL
X20
generations
X
HS HS RIHS inbred F1
(may containknockout
ortransgene)
modifier search
• How many RIHS do we need for effective fine-mapping?
• Are there other HS strategies to reduce genotyping…?
In Vitro HS (IVHS)
HS donor
recombinant
HS sperm F1
IVF
Fertilizeinbred dam
withHS sperm
meiosis
IVHS-1
genotypedonors at
high resolution
HS donor
recombinant
HS sperm F1
IVF
meiosis
IVHS-1
genotypedonors at
high resolution
HS donor
recombinant
HS sperm F1
IVF
pass1
pass2
F1 markers
meiosis
IVHS-2
HS donor
recombinant
HS sperm F1
IVF
treat as average of donor chromosomes
no furthergenotyping
meiosis
genotypedonors at
high resolution
Simulations• Compare strategies RIHS, IVHS-1, IVHS-2 by simulation
Simulations• Compare strategies RIHS, IVHS-1, IVHS-2 by simulation• Simulate 25cM chromosome with single additive QTL placed
randomly
Simulations• Compare strategies RIHS, IVHS-1, IVHS-2 by simulation• Simulate 25cM chromosome with single additive QTL placed
randomly• Type 100 SNP markers
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
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)
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
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
Detecting a significant locus• Pass rate = % times most significant marker interval has (corrected)
P-value less than 0.01
Detecting a significant locus• Pass rate = % times most significant marker interval has a corrected
P-value less than 0.01
consistent across population sizes
5%
Mapping accuracy for significant loci• Mean mapping error = average distance between true QTL and the
predicted locus
mapping error (cM)predicted QTL true QTL
Mapping accuracy for significant loci• Mean mapping error = average distance between true QTL and the
predicted locus
mapping error (cM)predicted QTL true QTL
Varying marker density and marker type• IVHS-1 strategy with 5%QTL, 1200 pups• Vary number of markers over a 3cM region
Varying marker density and marker type• IVHS-1 strategy with 5%QTL, 1200 pups• Vary number of markers over a 3cM region
Microsats betterMicrosats = SNPs
~0.05cM
Varying number of HS generations• IVHS-1 strategy with 5%QTL, 1200 pups
Varying number of HS generations• IVHS-1 strategy with 5%QTL, 1200 pups
optimum [5,15]
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)