SNP Based Parentage Assignment in Sheep: Application in Australian Flocks

PART 2: James Kijas 1, Julius van der Werf 6,7, Mohammad Ferdosi 6,7, Amy Bell 1,6, Sam Gill 6,8, Klint Gore 6, Jill Maddox 3 and John Henshall1,6.

1 CSIRO Livestock Industries, Australia. 2 AgResearch, New Zealand. 3 Port Melbourne, Australia. 4 USDA, USA. ,

PART 1: James Kijas 1, John McEwan 2, Shannon Clarke 2, Hannah Henry 2, Jill Maddox 3, Mike Heaton 4, for the International Sheep Genomics Consortium 5

CSIRO Livestock Industries, Australia. AgResearch, New Zealand. Port Melbourne, Australia. USDA, USA. ,Australia. 5 www.sheephapmpa.org. 6 Sheep Cooperative Research Center, Australia. 7 University of New England,Australia. 8 Meat Livestock Australia.

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Background and Aims

Current Industry Practises:

1. Syndicate matings- no information about paternity in large management groups- inability to track sire merit through progeny performance

2. Single Sire matings- requires dedicated infrastructure (eg fences)

3. Mothering up to construct dam pedigree- labour intensive (costly)- not that accurate

Aims for development of a SNP Panel for Parentage:

1. Develop a Core Panel of SNP with international application- robust on multiple genotyping platforms- high MAF across many sheep breeds- promote to ISAG

2. Trial a panel of Core + Performance SNP within Australian flocks- test needs to be $10 / animal to drive industry adoption- high exclusion in populations with high relatedness (sires and ewe base)

Outline

PART 1: Technical Development of a Core Panel of SNP

PART 2: Trialling a Core + Performance Panel of SNP within Australian Flocks

International Sheep Genomics Consortium

History of developing tools for the sheep genome

- reference genome assembly (now v3.0 !)- SNP50 BeadChip- HD SNP by January 2013

1. Core Panel

6021 SNP Identified by

Sanger Sequencing

GoldenGate Infinium

1536 SNP chip

22 breeds

2009

PLoS ONE 4:e4668

50K SNP chip

70+ breeds

2012

PLoS Biology 10:e1001258

Golden Gate and

Infinium testing

across populations

Prune on MAF, call

rate and position

Formatted for genotyping

Prune on inheritance

in IMF and trios

ISGC panel of 88 SNP

+ male specific oY1 SNP

6021 SNP Identified by

Sanger Sequencing40.0

50.0

60.0

Pro

po

rtio

n o

f S

NP

(%

)

International Sheep Genomics Consortium

History of developing tools for the sheep genome

- reference genome assembly (now v3.0 !)- SNP50 BeadChip

1. Core Panel

Golden Gate and

Infinium testing

across populations

Prune on MAF, call

rate and position

Formatted for genotyping

Prune on inheritance

in IMF and trios

ISGC panel of 88 SNP

+ male specific oY1 SNP

0.0

10.0

20.0

30.0

40.0

0 > 0.0 - < 0.1 ≥ 0.1 - < 0.2 ≥ 0.2 - < 0.3 ≥ 0.3 - < 0.4 ≥ 0.4 - < 0.5P

rop

ort

ion

of

SN

P (

%)

Minor Allele Frequency Bin

MAF Calculated across global collection of 74 breeds

MAF distributions for all 49034 SNP (red)

and the 89 SNP in the parentage panel (green)

Designed to work across diverse range of breeds

ISGC Parentage SNP Chr Mb Allele MAF Filter 1 Filter 2 Filter 3 Filter 4 5k Chip

Pos Re-seq Fluidigm SQ_AgR SQ_CLI

DU290101_408.1 1 7.8 A 0.337

DU518561_359.1 1 14.2 G 0.381

DU351298_316.1 1 69.6 A 0.445

DU232924_365.1 1 95.8 G 0.250

DU271929_382.1 1 97.5 A 0.483

DU502334_443.1 2 19.1 A 0.437

DU469454_586.1 2 26.2 G 0.394

DU425907_184.1 2 50.1 G 0.358

DU501115_497.1 2 62.8 A 0.239

DU492516_411.1 2 63.4 T 0.478

DU470875_383.1 2 91.5 G 0.357

1. Core Panel

Filter 1: Re-sequencingLoci Sanger re-sequenced

to examine sequence context.

(Mike Heaton USDA)

Filter 2: Fluidigm TestingGenotyping accuracy tested

using GT.96.96 format.

(Katica Ilic, Fluidigm)

Filter 3: Sequenom TestingDU470875_383.1 2 91.5 G 0.357

250506CS_*1 2 100.9 G 0.345

DU191879_495.1 2 157.6 A 0.335

DU480434_533.1 2 192.2 A 0.480

DU260201_585.1 2 226.7 A 0.422

DU503161_123.1 2 237.2 A 0.352

DU425259_620.1 3 21.4 A 0.461

DU231007_156.1 3 59.0 G 0.463

DU225323_218.1 3 91.0 A 0.467

DU260081_579.1 3 108.8 A 0.383

DU394537_289.1 3 181.6 G 0.371

CL635241_413.1 3 181.9 A 0.455

DU408817_431.1 3 205.0 A 0.343

DU202116_405.1 4 58.2 A 0.444

DU460511_423.1 4 61.1 G 0.443

DU305004_417.1 4 70.1 A 0.270

DU369175_467.1 4 73.0 G 0.375

DU446213_412.1 5 12.5 A 0.394

Filter 3: Sequenom TestingSNP formatted into SQ

multiplexes and tested

across pedigrees.

(JMcW, SC at AgResearch)

Filter 4: Sequenom TestingSNP formatted into SQ

multiplexes and tested

across pedigrees.

(JK, FD at MLA and CSIRO)

5K ChipMembership on 5K ILMN

Array

1. Core Panel: Accreditation Through ISAG

We (International Sheep Genomics Consortium) would like to propose:

1. That ISAG consider adopting the panel of 89 SNP as the standard for sheep

2. That ISAG hold a panel of DNA samples from multiple breeds (n = 96) with known genotype

3. Provide accreditation to interested providers of genotyping services

Test DNA

Test Genotypes

Accreditation

Discuss this afternoon

et al.,

Outline

1. Technical Development of the Core Panel of SNP

Recognise 89 SNP is unlikely to be sufficient for many applications. The model therefore likely to involve addition of ‘country specific’ SNP content

- Europeans interested in PNRP- Europeans interested in PNRP- Locally (Oz): Horn / Poll; pigmentation, muscularity etc

2. Trialling a Core + Performance Panel within Australian Flocks

2. Trialling a Core + Performance Panel within Australian Flocks

Sequenom Testing (CSIRO, MLA, Sequenom Brisbane)

- key methodology given price structure for delivery- mature technology imbedded in companies like GeneSeek

Designed 6 multiplexes containing 383 SNP (between 63 – 67 SNP / plex)

- 89 Core SNP- Performance SNP including:

� horn / poll � horn / poll � myostatin� hoof pigmentation

- Additional ‘filler’ SNP to make up the number of 383

Collected samples from 1600 animals (blood cards)

- 5 industry flocks (progeny, ewes, sires)

- mainly Merino

Genotyped using all 6 SQ plexs: assessed power

Defined assignment thresholds using simulation

- 1000 simulated lambs, each with one correct sire- LOD is the log of ratio (likelihood correct/incorrect)- the most likely sire given mLOD1(blue if correct, red if wrong)

- difference between mLOD1 and the next best sire is ∆1

- second most likely sire given mLOD2 (light blue)

2 SQ plexes

127 SNP (64 core)

Sires with high mLOD and Δ1

These all correct (no red dots)

2 SQ plexes

127 SNP (64 core)

Most likely sire is correct sire, however

the next best is very nearly as likely (low Δ)

The next best sire (light blue) would get

selected as the true sire in some cases

3 SQ plexes

190 SNP (84 core)Using 3 plexes makes big difference !

The next best sire (light blue) is never

selected as the true sire using the

defined threshold

Very high confidence for analysis

of the real data

Progeny that are unlikely to have

a sire in the group genotyped.

Producer confirmed this was possible

Most assignments made

where next best sire

is not very likely (Δ well

above threshold)

Simulated Data Real Data

Conclusions

1. Technical Development of the Core Panel of SNP

- identified technically robust SNP- suitable for use across sheep breeds- promoting the core panel to ISAG for accreditation

2. Trialling a Core + Performance Panel within Australian Flocks

- in our flocks, 190 SNP gives good confidence in assignment- expect to release a commercial test in 2013

Acknowledgements

Core Panel

John McEwanShannon ClarkeHannah HenryJill MaddoxMike HeatonThe International Sheep Genomics Consortium

Testing in Australian Flocks

Julius van der WerfDaniel Pomp and JeremyMohammad FerdosiAmy BellSam GillKlint GoreJill Maddox and John Henshall