1. From sequence data to genomic prediction

2. Course overview Day 1 Day 2 Day 3 Day 4 & 5 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

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

4. 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-> 50K (save $$$)
50K->800K
capture power of higher density?
Better persistence of accuracy
Sequence expensive, can we impute to full sequence data?

5. Core concept Identity by state (IBS) Identity by descent (IBD)
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

6. Causes of LD
A chunk of ancestral chromosome is conserved in the current population
Marker Haplotype 1 1 1 2

7. 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

8. 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)

9. 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)

10. Finding an IBD segment
Sire 2
Progeny ? 2

11. Sire 2
IBD segment
Progeny ? 2

12. Sire 2
Progeny 2 ?

13. 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)

14. 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

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

16. Population based imputation
Consider three markers, 4 reference haplotypes
0 1 1
0 1 0
1 0 1
0 0 1
Imputation?

17. Li and Stephens

18. Beagle

19. Imputation accuracy
Accuracy = correlation of real and imputed genotypes
Concordance = percentage (%) of genotypes called correctly

20. 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

21. 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): e doi: /journal.pone

22. Imputation accuracy Density of markers (extent of LD)
In Holstein Dairy cattle
3K -> 50K accuracy 0.93
7K -> 50K accuracy 0.98

23. Illumina Bovine HD array
We genotyped
898 Holstein heifers
47 Holstein Key ancestor bulls
After (stringent) QC 634,307 SNPs

24. Imputation 50K -> 800K
Holsteins

25. Imputation accuracy
Rare alleles?

26. Imputation accuracy
Relationship to reference?

27. Imputation accuracy
Effect of map errors?

28. 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

29. Why more power with imputation
Weigel et al. (2010)

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

31. 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

32. 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

33. An example A matrix……..
Pedigree
Animals 6 is a half sib of 4 and 5

34. 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)

35. 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 = pm’1n

36. Which individuals to sequence?
Example

37. Which individuals to sequence?
Then choose set of individuals to sequence (m) which maximise pm’1n
Step wise regression
Find single individual with largest pi, set ci to zero, next largest pi, set ci to zero…..
Genetic algorithm

38. Which individuals to sequence?
Then choose set of individuals to sequence (m) which maximise pm’1n
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

39. Which individuals to sequence?
Poll Dorset sheep

40. Imputation of full sequence data
Two groups of individuals
Sequenced individuals: reference population
Individuals genotyped on SNP array: target individuals

41. 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

42. Imputation of full sequence data
Variant calling
SamTools mPileup
Vcf file -> 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

43. Imputation of full sequence data
How accurate?

44. Run4.0 1000 bull genomes Run 4.0 1147 animals sequenced 27 breeds
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
Guelph Composite 30
Beef Booster 29
Alberta Composite 28
Montbeliarde
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
Unknown
Scottish Highland
Pezzata Rossa Italiana 1
Romagnola
Salers
Tyrolean Grey
Total
1147
1147 animals sequenced
27 breeds
20 Partners
Average 11X
CRV

45. 1000 bull genomes Run 4.0 36.9 million filtered variants
35.2 million SNP
1.7 million INDEL X

46. 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 46

47. Imputation of full sequence data47

48. Imputation of full sequence data48

49. Imputation of full sequence data49

50. 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 50

51. Imputation of full sequence data
Why so difficult to impute rare mutations?
BLAD
CVM
Location
Chr1:
Chr3:
Frequency
0.0014
0.0103
Bulls genotyped
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 51

52. 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 52

53. 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 53

56. 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