Recombination based population genomics

1
Recombination based population genomics

• Jaume Bertranpetit
• Marta Melé
• Francesc Calafell

Asif Javed Laxmi Parida
2
Recall IRiS

• Identification of Recombinations in Sequences
• IRiS
• is a computational method developed with
  biological insight
• detects evidence of historical recombinations
• minimizes number of recombinations in Ancestral
  Recombinational Graph (ARG)

3
Recotypes

• Two chromosomes share a recombination if the
  junction is co-inherited.

mutation edge
recombinationedge
extantsequence
4
Recotypes

• Two chromosomes share a recombination if the
  junction is co-inherited.

r1
a
b
5
Recotypes

• Two chromosomes share a recombination if the
  junction is co-inherited.

r1
r2
a
b
c
6
Recotypes

• Two chromosomes share a recombination if the
  junction is co-inherited.

r1 r2
a 1 0
b 1 0
c 0 1

r1
r2
a
b
c
7
Validity of inferred recombinations

• Comparison with sperm typing
• Computer simulated recombinations

8
in vitro
Chr 1 near MS32 minisatellite
Jeffreys et al. 2005 80 UK semen donor of North
European origin - Sperm typing- LDhat and Phase
(200 SNPs)
spermtyping
LDhat Phase
HapMap 2 CEU populationsimilar SNP density
IRiS
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in silico
Chromosomes

• HapMap 3 X chromosome data
• Select 2 chromosomes at random.
• Pick a random breakpoint.
• Create a new chromosome.
• Check if it is unique, add to the dataset.
• Run IRiS on the dataset to see if the breakpoint
  is detected.

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in silico
Chromosomes

• HapMap 3 X chromosome data
• Select 2 chromosomes at random.
• Pick a random breakpoint.
• Create a new chromosome.
• Check if it is unique, add to the dataset.
• Run IRiS on the dataset to see if the breakpoint
  is detected.

11
in silico
Chromosomes

• HapMap 3 X chromosome data
• Select 2 chromosomes at random.
• Pick a random breakpoint.
• Create a new chromosome.
• Check if it is unique, add to the dataset.
• Run IRiS on the dataset to see if the breakpoint
  is detected.

12
in silico
Chromosomes

• HapMap 3 X chromosome data
• Select 2 chromosomes at random.
• Pick a random breakpoint.
• Create a new chromosome.
• Check if it is unique, add to the dataset.
• Run IRiS on the dataset to see if the breakpoint
  is detected.

13
in silico
Chromosomes

• HapMap 3 X chromosome data
• Select 2 chromosomes at random.
• Pick a random breakpoint.
• Create a new chromosome.
• Check if it is unique, add to the dataset.
• Run IRiS on the dataset to see if the breakpoint
  is detected.

14
in silico
Chromosomes

• HapMap 3 X chromosome data
• Select 2 chromosomes at random.
• Pick a random breakpoint.
• Create a new chromosome.
• Check if it is unique, add to the dataset.
• Run IRiS on the dataset to see if the breakpoint
  is detected.

IRiS
recombination detected?
15
in silico
Chromosomes

• HapMap 3 X chromosome data
• Select 2 chromosomes at random.
• Pick a random breakpoint.
• Create a new chromosome.
• Check if it is unique, add to the dataset.
• Run IRiS on the dataset to see if the breakpoint
  is detected.

IRiS
recombination detected?
69 recombinations detectedAll detected
recombinations detect the correct sequenceNo
false positives
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Recombinomics

• Strong population structure
• Agreement with traditional methods
• FST vs. recombinational distance
• More informative than SNPs
• STRUCTURE

• PCA

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Regions

• 18 regions selected from HapMap 3
• X-chromosome in males (to avoid phasing errors)
• 50 KB away from known CNV and SD(to avoid
  genotyping errors)
• 50 KB away from genes(to avoid selection)
• at least 80 SNPs

Chromosomes LWK(43), MKK (88), YRI (88), ASW
(42), GIH (42), CHB (40), CHD (21), JPT(25),
MEX(21), CEU (74), TSI (40)
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Analysis

• For each region IRiS inferred recotypes for each
  chromosome
• 5166 recombinations were inferred
• 3459 co-occurred in at least two chromosomes

Recombination
Chromosome
r1 r2 r3 r4 r5 r6 r3459
LK1 0 1 1 0 0 0 0
LK2 1 0 1 1 0 0 0

LK43 1 0 1 0 0 0
MK1 0 1 0 0 1 1 1

TI40 0 0 0 0 0 1 0
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Analysis

• For each region IRiS inferred recotypes for each
  chromosome
• 5166 recombinations were inferred
• 3459 co-occurred in at least two chromosomes

Recombination
Chromosome
r1 r2 r3 r4 r5 r6 r3459
LK1 0 1 1 0 0 0 0
LK2 1 0 1 1 0 0 0

LK43 1 0 1 0 0 0
MK1 0 1 0 0 1 1 1

TI40 0 0 0 0 0 1 0
Recotype
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Agreement with LDhat
Each point represents a short haplotype segment
in HapMap CEU population
Spearman correlation 0.711pvalue lt10-30
recombination rate inferred by LDhat
number of recombinations inferred by IRiS
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Agreement with LDhat
Each point represents a short haplotype segment
in HapMap CEU population
Spearman correlation 0.711pvalue lt10-30
recombination rate inferred by LDhat
Correlation in hotspots c2 38.39 pvaluelt6x10-10
number of recombinations inferred by IRiS
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Recombinational distance between populations

• Two populations genetically closer will share a
  higher number of recombinations

Recombinational distance Correlation between
FST distance and recombinational distance for
the 18 region 0.35 0.75 with pvalues lt
0.025
RAB
DAB

1 -
RA RB -RAB
MDS All regions combined stress6.1
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PCA of population data
Recall recotypes
r1 r2 r3 r4 r5 r6 r3459
LK1 0 1 1 0 0 0 0
LK2 1 0 1 1 0 0 0

LK43 1 0 1 0 0 0
MK1 0 1 0 0 1 1 1

TI40 0 0 0 0 0 1 0
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PCA of population data
Recall recotypes
r1 r2 r3 r4 r5 r6 r3459
LK1 0 1 1 0 0 0 0
LK2 1 0 1 1 0 0 0

LK43 1 0 1 0 0 0
MK1 0 1 0 0 1 1 1

TI40 0 0 0 0 0 1 0
r1 r2 r3 r4 r5 r6 r3459
LK 14 7 4 9 0 1 0
MK 1 4 7 0 5 7 24

TI 0 1 7 1 0 0 1
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PCA of population data
The first two PCs capture 66.4 of the variance
r1 r2 r3 r4 r5 r6 r3459
LK 14 7 4 9 0 1 0
MK 1 4 7 0 5 7 24

TI 0 1 7 1 0 0 1
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PCA of recotypes

• more on this later

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Recotypes vs. SNPs

• Due to ascertainment bias gene diversity does not
  reflect population structure

results similar to Conrad 07
Percentage of variance
SNPs Recotypes
Across groups 9 6
Within groups 4 1
Within populations 87 93
Normalized comparison linearly scaled to 0,1
using 21 samples per population
in agreement with Lewontin 72
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from SNPs to haplotypes to recotypes(a STRUCTURE
comparison)
K2
SNPs
haplotypes
recotypes
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from SNPs to haplotypes to recotypes(a STRUCTURE
comparison)
K3
SNPs
haplotypes
recotypes
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from SNPs to haplotypes to recotypes(a STRUCTURE
comparison)
K4
SNPs
haplotypes
recotypes
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from SNPs to haplotypes to recotypes(a STRUCTURE
comparison)
K5
SNPs
haplotypes
recotypes
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Africa within global genetic variation
Structure k4
minority African specific component
Avg. Number of recombinations in 21 random
chromsomes
Out of Africa hypothesisFounders effect
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Genetic variation within Africa
Structure k5
Maasai specificminor component

• Subsaharan Maasai are distinct among Africans.
• African-American exhibit stronger
  recombinational affinity with African populations
  than European populations. (Parra 98)

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Genetic variation outside Africa
Structure k5
Avg. Number of recombinations in 21 random
chromsomes

• Outside Africa, Gujarati and Japanese exhibit
  the highest and lowest number of recombinations
  respectively.
• Gujarati Indians show intermediate position
  between Europeans and East Asians.

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Venturing outside the X-chromosome

• Benefits
• The bigger picture
• More regions and hence more information
• Challenges
• Higher number of recombinations makes the picture
  murkier
• Phasing errors

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Regions

• 81 regions selected from HapMap 3
• 50 KB away from known CNV and SD(to avoid
  genotyping errors)
• 50 KB away from genes(to avoid selection)
• at least 200 SNPs
• 25 samples per population(each sample has
  twochromosomes)

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Analysis

• For each region IRiS inferred recotypes for each
  chromosome
• 34140 recombinations were inferred
• For each sample the two recotypes were merged.

SNPs
recotypes
PCA plots
38
Quantifying population structure

• PCA and by k nearest neighbors is used to predict
  population of every sample

Perfectly classified
Africans
Non- Africans
classifiedwith errors
MKK
GIH
E. Asian
MEX
European
(0,7)
(3,13)
(8,13)
LKK
(4,3)
CHBCHD
JPT
CEU
TSI
YRI
ASW
Misclassification by (recotypes, SNPs)
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East Asian population

• Recotypes are more informative of underlying
  population structure.

SNPs
recotypes
PCA plots
40
in conclusion

• Recotypes
• show strong agreement with in silico and in
  vetro recombination rates estimates
• are highly informative of the underlying
  population structure
• provide a novel approach to study the
  recombinational dynamics