Fine mapping of complex traits in yeast mapping meiotic recombination across the genome
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Fine Mapping of Complex Traits in Yeast: Mapping Meiotic Recombination across the Genome. Wolfgang Huber EMBL - EBI. Proper chromosome segregation. Increase of genetic diversity. Gene A. Gene B. Gene C. Gene A. Gene b. Gene c. Gene a. Gene b. Gene c. Gene a. Gene B. Gene C.

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Fine mapping of complex traits in yeast mapping meiotic recombination across the genome
Fine Mapping of Complex Traits in Yeast: Mapping Meiotic Recombination across the Genome

Wolfgang Huber

EMBL - EBI


Meiotic recombination

  • Increase of genetic diversity

Gene A

Gene B

Gene C

Gene A

Gene b

Gene c

Gene a

Gene b

Gene c

Gene a

Gene B

Gene C

Meiotic Recombination


second end capture Recombination across the Genome

dHJ

noncrossover

crossover

DSBR Model of Recombination

DSB

strand resection

3’

3’

single-end invasion

D-loop


SDSA Recombination across the Genome

invading strand unwound

3d CO

pathway

nicked HJ

crossover

noncrossover

Current Molecular Model of Recombination

DSB

strand resection

3’

3’

single-end invasion

D-loop

DSBR

second end capture

dHJ

crossover

MC Whitby (2005)


Non random distribution of recombination across the genome

female Recombination across the Genome

average

male

Non-Random Distribution of Recombination Across the Genome

Human chr. 22q

Yeast chr. 3

Petes T.D., 2001

Baudat F. & Nicolas A., 1997

Recombination hotspots are small genomic regions where recombination events cluster and that are surrounded by large stretches of recombinationally suppressed DNA


Clinical isolates of s cerevisiae

The common lab yeast Recombination across the Genome

Clinical strain (YJM789)

Laboratory strain (S288c)

Clinical isolates of S. cerevisiae

Isolated from rotten fig in California in 1930s

Domesticated: related to baker's yeast, wine-making and beer-brewing yeasts

Isolated from immuno-compromised patients

Pathogenic in mouse model of systemic infection

Various fungal pathogenic characteristics: pseudohyphae, colony morphology switching

Ability to grow at >>37˚C – a virulence trait


SNP Recombination across the Genome

Genome I

A

C

G

A

T

G

Genome II

A

C

G

G

T

G

Hybridization Genome II

Hybridization Genome I

C

C

A

A

C

C

C

G

G

C

G

G

C

C

C

G

C

G

C

G

G

G

C

T

T

C

C

G

G

T

T

T

A

A

A

A

T

A

T

A

A

A

T

A

A

T

T

A

G

G

G

G

A

A

A

A

T

T

T

T

C

C

C

C

G

G

G

G

T

T

T

T

Genotyping with Microarrays

Perfect Match

Mismatch


1 tiling array for 2 yeast genomes

common Recombination across the Genome

S-specific

Y-specific

1 tiling array for 2 yeast genomes

*

5’

3’

Watson strand

8bp

*

3’

5’

Crick strand

4bp

25mer

10%

4%

86%

S288c

YJM789

291k

2,368k

108k

Wei et al., PNAS (2007)

3.4 Mio probes (25mers)

manufactured by Affymetrix

(probes)


Identification of previously unknown ncRNA and antisense transcripts and precise mapping of all transcripts

Antisense

CBF1

David et al., PNAS (2006)


Map all recombination events that occurred in 50 yeast meioses using high-density tiling microarrays


Experimental approach
Experimental Approach meioses using high-density tiling microarrays

S288c

YJM789

Diploid hybrid

Meiosis

Haploid spores

Data: 25 parental hybridizations, 200 offspring hybridizations


Single reporter methods
Single-reporter methods meioses using high-density tiling microarrays

De novo polymorphism detection

  • Winzeler et al.Science 281, 1998 (and others): ANOVA testing 1 = 1.

  • Borevitz et al.Genome Research 13, 2003: moderated t-test (SAM).

  • Brem et al. Science 296, 2002: moderated t-test, then cluster all data (parental and segregant) and discard SFPs for which clusters don’t separate the parental data.

  • Segregant genotyping (using polymorphims)

    • Use the estimated posterior probability of class membership (uniform prior on the classes):

    • Brem et al. augment this: are estimated from clustered data.


  • But we have multiple reporters per snp probe sets
    But we have multiple reporters per SNP: probe sets meioses using high-density tiling microarrays

    6: CTTCACTATTTGTACAGATCGCAAT

    5: CTAACTTCACTATTTGTACAGATCG

    Probe set: a set of reporters that exactly + uniquely map to a location and interrogate one polymorphism

    4: GGCCCTAACTTCACTATTTGTACAG

    2: GACTGGCCCTAACTTCACTATTTGT

    1: GGAGGACTGGCCCTAACTTCACTAT

    S96: CCTCCTGACCGGGATTGAAGTGATAAACATGTCTAGCGTTA

    YJM789: CCTCCTGACCGGGATTGAACTGATAAACATGTCTAGCGTTA

    3: GACTGGCCCTAACTTGACTATTTGT


    Multivariate analysis of probe set data parallel coordinate plots
    Multivariate analysis of probe set data meioses using high-density tiling microarraysparallel coordinate plots

    log2 intensity

    reporters in probe set


    Multivariate analysis of probe set data parallel coordinate plots1
    Multivariate analysis of probe set data meioses using high-density tiling microarraysparallel coordinate plots


    Multivariate methods
    Multivariate methods meioses using high-density tiling microarrays

    SNPScanner: Gresham et al., Science 311, 2006:

    • Model probe intensity xi with & without presence of SNP as function of

      • Probe GC content

      • Position of SNP within the probe

      • Nucleotides surrounding the SNP

    • Fit model parameters using two sequenced strains with known SNPs.

    • To genotype a segregant or new strain at a given base, compute a Bayes factor

      assumption: covariance matrix diagonal and same


    • But meioses using high-density tiling microarrays

    • neighbouring probes' data are not independent

    • covariances for the two genotypes are often quite different

    • training data is often not representative

    • SNPscanner method generates too many wrong calls

    •  a generalized multi-probe method


    Gt ssc a semi supervised model based genotyping algorithm
    GT-SSC: a semi-supervised, model-based genotyping algorithm meioses using high-density tiling microarrays

    An instance of EM algorithm:

    • Two-component mixture, 1 = 2 = 1/2.

    • (Xi,Yi) with array data Xi and class variable Yi. Yi known for parental arrays, unknown for segregants.

    • Assume X|Y multivariate normal.

    • E-step: initialize the unknown Y with some simple clustering, e.g. k-means, hierarchical agglomeration

    • Iteratively estimate parameters, E(Yi|Xi), parameters, ….

    • Classify segregant i based on final estimated E(Yi|Xi).


    GT-SSC (genotyping by semi-supervised clustering) meioses using high-density tiling microarrays

    An instance of the EM algorithm applied to

    multivariate Gaussian mixture modeling:

    simutaneously estimate class shapes and object class membership


    GT-SSC (genotyping by semi-supervised clustering) meioses using high-density tiling microarrays

    An instance of the EM algorithm applied to

    multivariate Gaussian mixture modeling:

    simutaneously estimate class shapes and object class membership


    GT-SSC (genotyping by semi-supervised clustering) meioses using high-density tiling microarrays

    An instance of the EM algorithm applied to

    multivariate Gaussian mixture modeling:

    simutaneously estimate class shapes and object class membership

    R package ss.genotyping


    Examples of probe set results
    Examples of probe set results meioses using high-density tiling microarrays


    Aberrant probe sets cross hybridization
    Aberrant probe sets (cross-hybridization?) meioses using high-density tiling microarrays


    Aberrant probe sets cross hybridization1
    Aberrant probe sets (cross-hybridization?) meioses using high-density tiling microarrays


    Filtering meioses using high-density tiling microarrays

    ambiguous individual genotype calls

    Aberrant probe sets

    Weakly separating probesets

    Imbalanced probesets

    Probe Sets

    Genotype Calls


    Segregation of 55 987 markers in a tetrad
    Segregation of 55,987 Markers in a Tetrad meioses using high-density tiling microarrays

    1 marker every 214 bp

    S288c

    YJM789

    Marker genotypes along chromosome


    52 tetrads meioses using high-density tiling microarrays


    Fine structure of meiotic breakpoints
    Fine structure of meiotic breakpoints meioses using high-density tiling microarrays

    4657 crossovers, 2766 conversions across 50 meioses.


    Complex events
    Complex events meioses using high-density tiling microarrays

    Crossover plus two conversion tracts - suggests that a single NCO resolution can produce evidence on both involved strands


    Complex events1
    Complex events meioses using high-density tiling microarrays

    Four overlapping conversion tracts.


    General characteristics
    General characteristics meioses using high-density tiling microarrays

    Crossover

    Conversion

    Size (bp)

    1.8% - 4.5% of genome is part of recombination events in a single meiosis (up to 544 Kb)

    Every chromosome had at least one crossover event



    Implications of a conversion hotspot non monotonous relationship between genetic and physical map
    Implications of A Conversion Hotspot: eventsNon-monotonous relationship between genetic and physical map

    tightly linked

    less tightly linked

    proximal sequence has greater genetic distance than distal sequence

    conversion

    hotspot


    Ty elements
    Ty elements events


    Recombination hotspots have eventslower density of SNPs across S. cerevisiae strains

    Recombination hotspots vs. SNP density (window = 2000)

    Fraction of significant recombination hotspots

    Recombination counts

    SNP density (Sanger unpublished)


    Interference
    Interference events

    Zki8

    Spo11

    Spo11

    Zki8

    Zip4

    Zip2

    Zip3

    Rad50

    Mre11

    Xrs2

    Zip1

    Zip1

    Dmc1/Rad51

    Mer3

    SDSA

    DSBR

    Msh4/Msh5

    Mus81

    Mms4

    Mlh1

    Mlh3

    crossover

    crossover

    noncrossover


    Meiosis in a msh4 null mutant
    Meiosis in a eventsmsh4 null mutant


    Crossover interference is reduced in msh4 mutant
    Crossover interference is reduced in msh4 mutant events

    40-60 kb

    60-80 kb

    Distance between adjacent crossovers (bp)


    Conclusions
    Conclusions events

    Used high-density tiling arrays to resequence >50 tetrads (>200 spores) from crossing of two phenotypically diverse strains

    1.8% - 4.5% of genome is part of recombination events in a single meiosis (up to 544 Kb)

    Recombination hotspots show evidence of allelic homogenization

    Crossover interference extends for 60-80 kb in wildtype and is reduced in msh4 mutant

    Conversion rates are unaffected in msh4 mutants questioning homeostasis between crossovers and conversions


    Acknowledgements
    Acknowledgements events

    • EBI

    • Alessandro Brozzi

    • David Jitao Zhang

    • Elin Axelsson

    • Ligia Bras

    • Tony Chiang

    • Audrey Kauffmann

    • Greg Pau

    • Oleg Sklyar

    • Mike Smith

    • Jörn Tödling

    EMBL HD

    Lars Steinmetz

    Julien Gagneur

    Zhenyu Xu

    Sandra Clauder-Münster

    Fabiana Perocchi

    Wu Wei

    Eugenio Mancera Ramos

    Richard Bourgon

    • The contributors to R and Bioconductor projects


    %DSB events

    DSBs Map

    Baudat F. & Nicolas A., 1997

    SGD Map

    cM/Kbp

    Kbp

    www.yeastgenome.org

    Comparison of Recombination Maps

    crossover

    conversion

    Our Maps

    count


    Benchmark snpscanner gts
    Benchmark SNPScanner - GTS events

    • 233 Affymetrix yeast tiling arrays from Steinmetz group:

      13 S288, 12 YJM789: training data

      52 tetrads of crosses: to be genotyped

    • Same post-processing/filter


    Gt ssc vs snpscanner
    GT-SSC vs SNPScanner events

    arrays

    genomic position (markers)




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