From sequence data to genomic prediction

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From sequence data to genomic prediction
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Course overview

• Day 1
• Introduction
• Generation, quality control, alignment of
  sequence data
• Detection of variants, quality control and
  filtering
• Day 2
• Imputation from SNP array genotypes to sequence
  data
• Day 3
• Genome wide association studies with SNP array
  and sequence variant genotypes
• Day 4 5
• Genomic prediction with SNP array and sequence
  variant genotypes (BLUP and Bayesian methods)
• Use of genomic selection in breeding programs

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Imputation

• Why impute?
• Approaches for imputation
• Factors affecting accuracy of imputation
• Does imputation give you more power?
• Imputation to whole genome sequence variant
  genotypes

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Why impute?

• Fill in missing genotypes from the lab
• Merge data sets with genotypes on different
  arrays
• Eg. Affy and Illumina data
• Impute from low density to high density
• 7K-gt 50K (save )
• 50K-gt800K
• capture power of higher density?
• Better persistence of accuracy
• Sequence expensive, can we impute to full
  sequence data?

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Core concept

• Identity by state (IBS)
• A pair of individuals have the same allele at a
  locus
• Identity by descent (IBD)
• A pair of individuals have the same alleles at a
  locus and it traces to a common ancestor
• Imputation methods determine whether a chromosome
  segment is IBD

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Causes of LD

• A chunk of ancestral chromosome is conserved in
  the current population

Marker Haplotype
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Core concept 2

• Any individuals in a population may share a
  proportion of their genome identical by descent
  (IBD)
• IBD segments are the same and have originated in
  a common ancestor
• The closer the relationship the longer the IBD
  segments
• Pedigree relationships

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Several methods for imputation

• Two main categories
• Family based
• Population based
• Or combination of the two
• Some of the most effective are Beagle (Browning
  and Browning, 2009), MACH (Li et al., 2010),
  Impute2 (Howie et al., 2009), AlphaPhase (Hickey
  et al 2011)

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Several methods for imputation

• Two main categories
• Family based
• Population based
• Or combination of the two
• Some of the most effective are Beagle (Browning
  and Browning, 2009), MACH (Li et al., 2010),
  Impute2 (Howie et al., 2009), AlphaPhase (Hickey
  et al 2011)

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Finding an IBD segment
Sire
Progeny
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Sire
IBD segment
Progeny
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Sire
Progeny
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Several methods for imputation

• Two main categories
• Family based
• Population based (exploits LD)
• Or combination of the two
• Some of the most effective are Beagle (Browning
  and Browning, 2009), MACH (Li et al., 2010),
  Impute2 (Howie et al., 2009), AlphaPhase (Hickey
  et al 2011)

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Population based imputation

• Hidden Markov Models
• Has hidden states
• For target individuals these are map of
  reference haplotypes that have been inherited
• Imputation problem is to derive genotype
  probabilities given hidden states, sparse
  genotypes, recombination rates, other population
  parameters

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Population based imputation
Reference population
Target population
Marchini J, Howie B. Genotype imputation for
genome-wide association studies. Nat Rev Genet.
2010 11499-511.
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Population based imputation

• Consider three markers, 4 reference haplotypes
• 0 1 1
• 0 1 0
• 1 0 1
• 0 0 1
• Imputation?

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Li and Stephens
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Beagle
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Imputation accuracy

• Accuracy correlation of real and imputed
  genotypes
• Concordance percentage () of genotypes called
  correctly

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Imputation accuracy

• Depends on
• Size of reference set
• bigger the better!
• Density of markers
• extent of LD, effective population size
• Frequency of SNP alleles
• Genetic relationship to reference

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• Table 6. Accuracy of imputation from BovineLD
  genotypes to BovineSNP50 genotypes for
  Australian, French, and North American breeds.

Boichard D, Chung H, Dassonneville R, David X, et
al. (2012) Design of a Bovine Low-Density SNP
Array Optimized for Imputation. PLoS ONE 7(3)
e34130. doi10.1371/journal.pone.0034130 http//ww
w.plosone.org/article/infodoi/10.1371/journal.pon
e.0034130
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Imputation accuracy

• Density of markers (extent of LD)
• In Holstein Dairy cattle
• 3K -gt 50K accuracy 0.93
• 7K -gt 50K accuracy 0.98

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Illumina Bovine HD array

• We genotyped
• 898 Holstein heifers
• 47 Holstein Key ancestor bulls
• After (stringent) QC 634,307 SNPs

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Imputation 50K -gt 800K

• Holsteins

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Imputation accuracy

• Rare alleles?

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Imputation accuracy

• Relationship to reference?

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Imputation accuracy

• Effect of map errors?

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Why more power with imputation

• High accuracies of imputation demonstrate that we
  can infer haplotypes of animal genotyped with
  e.g. 3K accurately
• But potentially large number of haplotypes
• With imputed data can test single snp, only use 1
  degree of freedom, rather than number of
  haplotypes

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Why more power with imputation

• Weigel et al. (2010)

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Imputation

• Why impute?
• Approaches for imputation
• Factors affecting accuracy of imputation
• Does imputation give you more power?
• Imputation to whole genome sequence variant
  genotypes

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Which individuals to sequence?

• Those which capture greatest genetic diversity?
• Select set of individuals which are likely to
  capture highest proportion of unique chromosome
  segments

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Which individuals to sequence?

• Let total number of individuals in population be
  n, number of individuals that can be sequenced be
  m.
• A average relationship matrix among n
  individuals, from pedigree

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• An example A matrix..

Pedigree
Animals 6 is a half sib of 4 and 5
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Which individuals to sequence?

• Let total number of individuals in population be
  n, number of individuals that can be sequenced be
  m.
• A average relationship matrix among n
  individuals, from pedigree
• c is a vector of size n, which for each animal
  has the average relationship to the population
  (eg. Sum up the elements of A down the column for
  individual i, take mean)

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Which individuals to sequence?

• If we choose a group of m animals for sequencing,
  how much of the diversity do they capture
• pm Am-1cm
• Where Am is the sub matrix of A for the m
  individuals, and cm is the elements of the c
  vector for the m individuals
• Proportion of diversity pm1n

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Which individuals to sequence?

• Example

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Which individuals to sequence?

• Then choose set of individuals to sequence (m)
  which maximise pm1n
• Step wise regression
• Find single individual with largest pi, set ci to
  zero, next largest pi, set ci to zero..
• Genetic algorithm

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Which individuals to sequence?

• Then choose set of individuals to sequence (m)
  which maximise pm1n
• Step wise regression
• Find single individual with largest pi, set ci to
  zero, next largest pi, set ci to zero..
• Genetic algorithm
• No A? Use G

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Which individuals to sequence?

• Poll Dorset sheep

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Imputation of full sequence data

• Two groups of individuals
• Sequenced individuals reference population
• Individuals genotyped on SNP array target
  individuals

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Imputation of full sequence data

• Steps
• Step 1. Find polymorphisms in sequence data
• Step 2. Genotype all sequenced animals for
  polymorphisms (SNP, Indels)
• Step 3. Phase genotypes (eg Beagle) in sequenced
  individuals, create reference file
• Step 4. Impute all polymorphisms into
  individuals genotyped with SNP array

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Imputation of full sequence data
Variant calling SamTools mPileup Vcf file -gt
filter (number forward /reverse reads of each
allele, read depth, quality, filter number of
variants in 5bp window)
Create BAM files 1. Filter reads on quality
score, trim ends 2. Remove PCR duplicates 3.
Align with BWA
Beagle Phasing in Reference Input genotype probs
from Phred scores QC with 800K
BAM
Reference file for imputation
Analysis Genome wide association Genomic
selection
Beagle Imputation in Target SNP array data in
target population
Genotype probabilities
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Imputation of full sequence data

• How accurate?

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Run4.0 1000 bull genomes Run 4.0
Breed/Cross Number
Holstein (Black and White) 288
Simmental (Dual and Beef) 216
Angus (Black and Red) 138
Jersey 61
Brown Swiss 59
Gelbvieh 34
Charolais 33
Hereford 31
Limousin 31
Guelph Composite 30
Beef Booster 29
Alberta Composite 28
Montbeliarde 28
AyrshireFinnish 25
Normande 24
Holstein (Red and White) 23
Swedish Red 16
Danish Red 15
Other Crosses 11
Belgian Blue 10
Piedmontese 5
Eringer 2
Galloway 2
Unknown 2
Scottish Highland 2
Pezzata Rossa Italiana 1
Romagnola 1
Salers 1
Tyrolean Grey 1
Total 1147

• 1147 animals sequenced
• 27 breeds
• 20 Partners
• Average 11X

CRV
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1000 bull genomes Run 4.0

• 36.9 million filtered variants
• 35.2 million SNP
• 1.7 million INDEL

X
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Imputation of full sequence data

• Accuracy?
• Chromosome 14
• Remove 50 Holsteins, 20 Jerseys from data set
• Reduce genotypes to 800K for these animals
• Impute full sequence using rest of animals as
  reference

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Imputation of full sequence data
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Imputation of full sequence data
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Imputation of full sequence data
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Imputation of full sequence data

• Why so difficult to impute rare mutations?
• Examples Complex Veterbral Malformation (CVM) and
  Bovine Leukocyte Deficiency (BLAD)
• All cases of CVM trace back to Ivanhoe Bell
• BLAD traces to Osbornedale Ivanhoe

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Imputation of full sequence data

• Why so difficult to impute rare mutations?

BLAD CVM
Location Chr1145114963 Chr343412427
Frequency 0.0014 0.0103
Bulls genotyped 5987 5987
Imputed correctly 5970 5836
Accuracy 0.9972 0.9748
Carriers 17 123
Carriers correctly imputed 13 5
Prop. Carriers correctly imputed 0.765 0.041
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Imputation of full sequence data

• Why so difficult to impute rare mutations?
• The BLAD mutation is in a unique 250kb haplotype,
  which does not occur in any non-carriers
• The CVM mutation is in a 250kb haplotype which
  occurs in many non carriers, and also occurs in
  breeds without mutation
• Hypothesis BLAD mutation occurred on rare
  haplotype, while CVM a recent mutation that
  occurred on a common haplotype background

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Imputation of full sequence data

• Computationally efficient strategies
• Beagle run imputation in chromosome segments,
  say 5MB with 0.5MB overlap (to avoid edge
  effects)
• Fimpute much faster than Beagle, used to impute
  32,500 animals from 800K to 16 million SNP!
• Does not give probabilties
• Beagle phasing Minimac

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Conclusion

• Impute
• to fill in missing genotypes
• low density to high density to save
• Accuracy depends on size of reference, effective
  population size, relationship to reference,
  marker density
• Imputation to sequence possible, relatively low
  accuracies for rare alleles
• Use genotype probabilities from imputation in
  GWAS and genomic prediction