Fine mapping qtls using recombinant inbred hs and in vitro hs
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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. 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

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
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


Cost of mapping with hs
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
Recombinant Inbred HS (RIHS)

X

20

generations

HS

HS

RIHS


Recombinant inbred hs rihs1
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


Recombinant inbred hs rihs2
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


Rihs for mapping modifier qtl
RIHS for mapping modifier QTL

X

X

20

generations

inbred

HS

HS

RIHS

F1

(may contain

knockout

or

transgene)

modifier search



In vitro hs ivhs
In Vitro HS (IVHS)

meiosis

Fertilize

inbred dam

with

HS sperm

IVF

recombinant

F1

HS sperm

HS donor


Ivhs 1
IVHS-1

meiosis

IVF

recombinant

genotype

donors at

high resolution

F1

HS sperm

HS donor


Ivhs 11
IVHS-1

meiosis

IVF

recombinant

pass

1

pass

2

genotype

donors at

high resolution

F1

HS sperm

HS donor

F1 markers


Ivhs 2
IVHS-2

meiosis

IVF

no further

genotyping

recombinant

genotype

donors at

high resolution

F1

HS sperm

HS donor

treat as average of

donor chromosomes


Simulations
Simulations

  • Compare strategies RIHS, IVHS-1, IVHS-2 by simulation


Simulations1
Simulations

  • Compare strategies RIHS, IVHS-1, IVHS-2 by simulation

  • Simulate 25cM chromosome with single additive QTL placed randomly


Simulations2
Simulations

  • Compare strategies RIHS, IVHS-1, IVHS-2 by simulation

  • Simulate 25cM chromosome with single additive QTL placed randomly

  • Type 100 SNP markers


Simulations3
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


Simulations4
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)


Simulations5
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
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
Detecting a significant locus

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


Detecting a significant locus1

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


Mapping accuracy for significant loci
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 loci1
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
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 type1

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


Varying number of hs generations
Varying number of HS generations

  • IVHS-1 strategy with 5%QTL, 1200 pups


Varying number of hs generations1
Varying number of HS generations

  • IVHS-1 strategy with 5%QTL, 1200 pups

optimum [5,15]


Conclusions
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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