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)