fine mapping of complex traits in yeast mapping meiotic recombination across the genome
Download
Skip this Video
Download Presentation
Fine Mapping of Complex Traits in Yeast: Mapping Meiotic Recombination across the Genome

Loading in 2 Seconds...

play fullscreen
1 / 46

Fine Mapping of Complex Traits in Yeast: Mapping Meiotic Recombination across the Genome - PowerPoint PPT Presentation


  • 217 Views
  • Uploaded on

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.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Fine Mapping of Complex Traits in Yeast: Mapping Meiotic Recombination across the Genome' - darren


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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

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

Meiotic Recombination
slide4

second end capture

dHJ

noncrossover

crossover

DSBR Model of Recombination

DSB

strand resection

3’

3’

single-end invasion

D-loop

slide5

SDSA

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

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

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

slide8

SNP

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

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)

slide10

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

Antisense

CBF1

David et al., PNAS (2006)

slide11

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

experimental approach
Experimental Approach

S288c

YJM789

Diploid hybrid

Meiosis

Haploid spores

Data: 25 parental hybridizations, 200 offspring hybridizations

single reporter methods
Single-reporter methods

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

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 dataparallel coordinate plots

log2 intensity

reporters in probe set

multivariate methods
Multivariate methods

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

slide18

But

  • 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

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).
slide20

GT-SSC (genotyping by semi-supervised clustering)

An instance of the EM algorithm applied to

multivariate Gaussian mixture modeling:

simutaneously estimate class shapes and object class membership

slide21

GT-SSC (genotyping by semi-supervised clustering)

An instance of the EM algorithm applied to

multivariate Gaussian mixture modeling:

simutaneously estimate class shapes and object class membership

slide22

GT-SSC (genotyping by semi-supervised clustering)

An instance of the EM algorithm applied to

multivariate Gaussian mixture modeling:

simutaneously estimate class shapes and object class membership

R package ss.genotyping

slide26

Filtering

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

1 marker every 214 bp

S288c

YJM789

Marker genotypes along chromosome

fine structure of meiotic breakpoints
Fine structure of meiotic breakpoints

4657 crossovers, 2766 conversions across 50 meioses.

complex events
Complex events

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

complex events1
Complex events

Four overlapping conversion tracts.

general characteristics
General characteristics

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:Non-monotonous relationship between genetic and physical map

tightly linked

less tightly linked

proximal sequence has greater genetic distance than distal sequence

conversion

hotspot

slide36

Recombination hotspots have lower 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

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

crossover interference is reduced in msh4 mutant
Crossover interference is reduced in msh4 mutant

40-60 kb

60-80 kb

Distance between adjacent crossovers (bp)

conclusions
Conclusions

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
  • 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
slide42

%DSB

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
  • 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

arrays

genomic position (markers)

ad