Sheep Parentage

- a comparison of technologies

Shannon Clarke, Ken G Dodds, Rudiger Brauning, Tracey Van Stijn, Rayna Anderson, Suzanne Rowe and John McEwan

AgResearch Ltd, Invermay Agricultural Centre, Mosgiel, New Zealand

Accurate pedigree information

• critical to animal breeding and livestock production systems • rate of genetic gain; estimate breeding values accurately

• management of inbreeding

• Traditionally• pedigree information -breeder records

• Single sire matings- requires dedicated infrastructure (eg fences)

• Mothering up to construct dam pedigree- labour intensive (costly); accuracy?

• However, in mob/syndicate matings- no information about paternity in large management groups; inability to track sire merit through progeny performance

• Microsatellites (simple sequence repeats/short tandem repeats)

• Other benefits• Feed saved

• not spreading out ewes saves around $5 of feed costs by better control of feed in autumn

• also can farm animals in commercial environment

• Pregscan• technology very dependent on use of pregscanners for NLB and also for fetal aging.

• This plus management (lambing by expected lambing NLB and birth date while mixing up mating groups allows proper contemporary group formation)

• allows estimation of ram fertility/mating success, work underway but heritable and repeatable trait.

• Abundance of available genomic data + development of high throughput genotyping platforms

• SNP is DNA marker of choice for pedigree (and genomic selection studies)

• SNPs allow for standardisation between laboratories; a property that is crucial for developing an international set of markers for traceability studies.

Accurate pedigree information

SNP Panels for Parentage assignment

International Sheep Genomics Consortium (ISGC)

• Development of genomic tools (genome assemblies,

various SNP chip arrays…...FAANG)

• Majority of parentage SNP panels by various groups as

a result of initial ISGC tool development

Kijas et al

SNP Panels for Parentage assignment

Core set + country specific SNPs

263

48

35

1

2

2

80

33

CSIRO

USDA

ISGC

AgR

ISGC Core Panel 88 SNPs + 1 oY1

• Selected using population allele

frequencies

• 70 breeds

• 5 continents

• Bias to high MAFs

• use across numerous breeds

• Further information and SNP list

for download available at

www.sheephapmap.org

ISGC Illumina 15K array• Designed to enable accurate imputation to

HD chip

• a combination of the current AgResearch

~7K and Australian sheep CRC ~11K LD

chips with additional added content

• ~12K of imputation SNPs spaced

across the genome

• ~800 parentage SNPs from various

platforms

• ISGC, AgR, CSIRO, USDA, INRA

• ~2000 SNPs for enhanced imputation

SNPs in regions of interest

• Literature and production SNPs

ISGC Illumina 15K arrayMAF in NZ sheep

All SNPs Parentage SNPs

Sheep parentage- a comparison of

technologies• Many genotyping platforms

• Offer different multiplex levels, throughput, call rates

• Mass spec

• Array platforms

• Targeted sequencing

• Oligo probes

• ….

• SNP selection critical

• Cost per SNP decreasing

Cost vs Utility?

SNP parentage panel + some production/specific

trait SNPs (100’s-1000’S)

VS

SNP set that enables not only pedigree, inbreeding

but also genomic selection (10,000s-100,000s)

-cost similar to current parentage panels

Genotyping-by-sequencing

• Two “categories”-

• oligo directed re-sequencing of specific regions -

targeted

• reduced representational re-sequencing- restriction

enzyme digests- “random”

• Both use high levels of adaptor sequence

barcoding and multiplexing (48-2068…)

samples/lane

• Number of SNPs interrogated: 100’s to > 1 Million.

Aim to have a high throughput, reproducible and cost

effective GBS method.

• Tissue sample collection

• DNA extraction

• Library preparation

• Sequencing

• Analysis• Parentage assignment, inbreeding

• Genomic selection (GBLUP)

• GWAS studies

• Short processing time for results to industry

Industry application

Why “random”?

• Working with a number of species (~ method for all)• Industry:

• ruminants, shell & fin fish, forage (clover & rye-grass)

• Research:

• Fur seal, crow, tuatara, albatross, robin, weevils, bee

• Sheep (test species)

• Genome assembly

• HD SNP chip

• High throughput/quality DNA extraction

• price competitive with existing DNA based parentage assays

• all animals in the sire breeding tier are genotyped?

Why “random”?• little up-front development cost compared to array &

targeted GBS system• No-oligo design/purchase required

• Cost competitive: minor agricultural species & natural populations

• Good for species with high genetic diversity

• potential advantage: the elimination of ascertainment bias• relevant for population genetics studies- allows use of the

full range of allele frequency spectrum based analytical methods.

• High molecular weight DNA

• 260/280 > 1.8

• Consistent amount of DNA extracted (CV <20%)

1. High throughput DNA extraction from ear/fin clip tissue punch

Step 2. GBS Library preparation and purification

restriction enzyme based

Figure from Elshire et al 2011

• Utilises Elshire et al 2011 GBS method with the addition of a library

purification step utilising the Pippin Prep (SAGE Science) to further size

select DNA sequencing library.

• Accurate nano- robotic systems employed throughout.

• HiSeq 2500 V4 chemistry

• Single end reads (1x100)

Step 3. SequencingStep 4. Bioinformatic and

statistical analysisQC

+ve/-ve controls

Species

Reads/bar code

Allflex

TSUClarke et al., 2014 PLoS ONE

Average concordance observed between the HD SNP chip and the

genotypes identified by GBS and WGS (~20x).

Not enough sequencing reads to support heterozygous

genotype.

✓ Adjust genotyping method to increase sequencing

read depth

• Pippin-reduce GBS library size

• Double digests

OR

✓ Develop statistical methods for GBS data

• kinship using GBS with depth adjustment (KGD)

Genotyping-by-sequencing

Genotype sampling-by-sequencing

Allele typing-by-sequencing

Sampling…

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C/C

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Sampling…

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1/1 0/0 1/0

…..ACGCACTG……

1/0

…..ACGCACTG……

2/0

…..ACGCACTG……

…..ACGCACTG……

0/3

…..ACGTACTG……

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2/0

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T/C T/T C/C

T/C T/C T/T

T/C C/C T/C

T/C */* C/C

C/C C/C T/T

T/T C/C T/C

Actual

Allele count ☺ Traditional

• unbiased estimates of relatedness via method 1 of VanRaden (2008) adjusted

to account for sequence read depth at each individual SNP location including

SNPs with zero/missing reads KGD

• allows GBS to be applied at read depths which can be chosen to optimise the

information obtained.

• SNPs with excess heterozygosity, often due to (partial) polyploidy or other

duplications can be filtered based on a simple graphical method.

Van Raden 2008 J Dairy Sci. 91:4414-23

“Fin plot “- relationship of Hardy-Weinberg disequilibrium, MAF

& SNP depth for Atlantic salmon.

• Various filters based on this plot

• proportional closeness to

lower boundary

• cut-off on level of Hardy-

Weinberg disequilibrium.

• best results (relatedness

estimates closest to pedigree-

based values)

• remove SNPs with HW

disequilibrium below -0.05

Relatedness estimation uses:

• Parentage

• Animal & plant breeding (Genetic merit)

• Historically pedigree based (expected relatedness)

• Can now use genomic relatedness matrix (GRM)

• Genetic diversity

• E.g. PCA plots via GRM

• Population genetics

• Use GRM to estimate heritabilities without pedigrees

Pst 1 method comparisonsPstI

94/lane

PstI

376/lane

PstI/MspI PstI/MspI-Y

adapter

# SNPs 56,888 66,385 95,390 106,930

Mean co-call rate (for

sample pairs)

0.82 0.42 0.64 0.57

Min co-call rate (for

sample pairs):

0.72 0.02 0.17 0.30

Mean sample depth 7.90 1.64 3.52 2.82

Mean self-relatedness

(G5 diagonal):

1.05 1.16 1.07 1.09

Samples/lane 94 376 94 94

GBS vs SNP chip

96 samples → 384 samples/lane: Sheep

384 samples/lane

96 s

am

ple

s/lane

Relatedness-best sire match

5 sires

89 progeny

1 missing sire

96 well plate reaction

VS

384 well plate reaction

$$ reduction

Example-Dairy Sheep

• new dairy sheep flock with an East Friesian

genetic base

• phenotypic records on 3000 ewes but no pedigree

information.

• 300 ram lambs and 50 older rams available for

selection candidates – which to breed from?

→ Genotype ewes and rams, generate GRM to estimate

ram breeding values

• assess GBS by comparing GRMs from GBS and

ISGC15k SNP chip & subsequent eBVs.

Example-Dairy Sheep⚫ sampdepth ≤ 0.3

⚫ 0.3< sampdepth ≤ 1

⚫ 1< sampdepth ≤2

⚫ sampdepth>2

Genomic relationships and BVs are highly correlated

-10

-8

-6

-4

-2

0

2

4

6

8

-15 -10 -5 0 5 10 15

Example-Dairy Sheep

Breeding Values kg Milk GBS vs Chip

GBS 15k Chip

Genome Required No Yes

Development Costs Minimal ~US$3k Moderate >US$60k

Species/Population

DependentNo Yes

DNA quality and

QuantitationHigh ~4 days Moderate ~2 days

SNPs called Untargeted Fixed & reliable

Throughput 188/376 per lane 24 per chip

Cost per sample US$16/25 US$35

Number of SNPs>60,000 (20-30/Mbp)

ave. depth 3.4 reads @ 188

per lane15,000 (~5/Mbp)

Minor allele frequency Greater range Biased

Example-Dairy Sheep

• Deer, Goat, Salmon, Mussel, Forage

• Transitioning from SSR parentage to GBS

• Commercial this season

• Parentage-GRM-pedigree for BLUP

• Inbreeding

• (genomic selection)

From MS GBS and GS

Back to Sheep…

“Enhanced” parentage chip

Illumina Infinium XT

• 96-sample BeadChip

• multispecies

• ~800 Parentage SNPs from the 15K chip

• Literature / productions SNPs

• Enhanced imputation SNPs

Aim is to have all animals in breeding tier genotyped

Contact:

shannon.clarke@agresearch.co.nz

john.mcewan@agresearch.co.nz

Summary

• KGD• produces bias free genomic relationship matrices, based on

allele read depths

• can estimate:• Breed composition

• Pedigree

• Traceability

• Inbreeding

• Co-ancestry

• Included directly in existing mixed models to estimate breeding values.

• Allows all animals in the sire breeding tier to be genotyped.

• One potential advantage: elimination of ascertainment bias which plague array and oligo-based technologies• relevant for population genetics studies: allows use of allele

frequency spectrum based analytical methods

Acknowledgments: the animal genomics team

John McEwan

Rudi Brauning

Ken Dodds

Tracey Van Stijn

Rayna Anderson

Suzanne Rowe

'Genomics for Production & Security

in a Biological Economy' C10X1306FIQ Systems – Plate to

Pasture PGP06-09020