Current and Future Role of DNA Technology in the Livestock Industry

Mark Allan, PhD

Beef Cattle Geneticist

ARS, U.S. Meat Animal Research Center

Clay Center, NE

Young and Changing Technology

Seed Stock

Commercial Cow/calf

Finishing Phase

Family pedigree Birth date Birth weight Individual calving difficulty score Weaning weight Yearling weightUltrasound - ~yearlingHip heightMature wt body condition scoresUdder and teat - AmesBreeding records Carcass dataScrotalDocility

Information Recorded on Individual Animal

Traditional Selection Works Well

Selection Practices– Visual– Performance Data ***– EPDs****– Pedigree – DNA Marker Information– Modeling– Economic Indexes

Selection Index

Different indexes for different phases of production!

$VALUE INDEXES

•Weaned Calf Value ($W) •Grid Value ($G) •Quality Grade ($QG) •Yield Grade ($YG)•Beef Value ($B)

How Do We Collect DNA?

• Blood• Hair Roots• Saliva• Skin• Semen • Fecal Samples• Other Tissues

Chromosomes

G C

A T

Gene AAllele 1, Allele 2

Bases

Marker-Assisted Selection (MAS)

-Inherited Diseases

-Coat Color

-Embryo Sexing

-Horned/Polled

-Quantitative Traits

-Feed Efficiency, Growth, Reproduction, Carcass Traits

Marker-Assisted Management (MAM)

Populations QTL Scans

• Nellore (Indicus)/Hereford sire n=547

• Brahman/Angus sire n=620

• Belgian Blue/MARCIII sire n=246

• Piedmontese/Angus sire n=209

x

x

x

x

MARCIII

Sires were mated to Angus, Hereford and

MARCIII females

Carcass Traits – Minor Success

Traitsa QTLb Chromosomesc

TEND 8 2 4 5 7 8 9 10 11 15 18 19 20 29

MAR 24 2 3 4 5 6 7 8 9 10 12 13 14 16 17 18 20 21 23 26 27 29

PCHOICE 7 1 2 5 11 14 19 26

FATD 24 1 2 3 5 6 7 8 14 16 19 21 23

BFAT EBV 12 5 6 14 19 21 23

AFAT 2 1 19

EEFAT 1 19

FATTYD 3 1 18 26

RIBFAT 5 5 13 18 26

KPH 7 2 3 11 15 16 17

FAa 5 19

LMA 7 2 4 6 12 14 26

REA 6 2 12 14 19 21

RIBB 1 5

SWT 10 1 2 3 12 14 16 17 24

RPYD 13 1 2 3 5 9 12 13 18 19 26 29

CW 27 1 2 4 5 6 7 10 12 13 14 16 18 22 23 24 29

DP 8 1 5 10 13 16 24 29

YG 10 1 2 5 11 12 14 16 19 21 26

Phenotypes

Carcass Traitshot carcass wtfat depthmarbling scoreest. k & p fat, heart fatrib boneribfatribmusUSDA yield grade shear force

Predicted Carcass Traitsretail product yieldfat yieldwhole sale rib-fat yield

Growth Traitsbirth wtweaning wtyearling wtaverage daily gain

Reproductive TraitsFSH -malestesticular weighttesticular volumetwinning rateovulation rate

Discovery of QTL

Strategy to Identify Genes/Markers

Quantitative Trait Locus

(QTL)

CandidatePosition

Fine Mapping

Positional Candidate

Gene/MarkerValidation

Industry Application

Limiting

Need additional laboratory tools

Development of DNA Markers

DNA Marker

Biology

Differential gene expression

Genetically modified animals

Mutantmodels

Proteome analysis

Genome sequence

Comparative maps

Metanomics

Fine mapping Progeny testing

Gene candidates

Bioinformatics

CAPN1 Story

• QTL found on BTA29 for shear force in the Piedmontese/Angus sired population

• CAPN1 mapped to region

QTL

CAPN1x

Parentage Verification

?

m 2 P Sire 1 Sire 2

Progeny (P)

Animal Identification Youth Livestock Shows

Tracing Products

BSE

Washington

Announced by the USDA on December 23, 2003

First recorded case in the U.S.

APHIS requested our assistance on the DNA-based traceback

First Case BSE

Sequencing the Bovine Genome

PHASE1 - $53 million

• NHGRI - $25 million

• New Zealand - $1 million

• Texas - $5 to10 million

• National Cattlemen’s Beef Association,

Texas and South Dakota cattle producers - $820,000

Baylor College of Medicine-Human Genome Sequencing

Center

Hereford Cow from Miles City

SNPs- Single Nucleotide Polymorphisms

-Occur much more frequently throughout the genome

-2 alleles possible

ATGCAATTGCCACGTTGCAAT

ATGCAATTGCTACGTTGCAAT

ATGCAATTGCC/TACGTTGCAAT

SNP Genotyping

Allele 1

Allele 2

Primer

Linkage Disequilibrium- LD*

*

*

*

*

*

*

**

*

*

**

*

Illumina Infinium Bovine BeadChip

~ 54,008 SNP markers across the bovine genome

- On average SNP every <67,000 base pair

- Discovery SNP includes many breeds

BARCUSMARCUniversity of MissouriUniversity of Alberta

(Van Tassell et al., 2008 Nature Methods)

Genotyping of Animals GPEVII

• 2,020 F12 animals with feed intake record for ~52,156 SNP/animal• 152 GPEVII AI sires• 73 GPEVII F1 sires• 580 GPEVII F1 steers• 150 GPEVII F1 dams

~155,164,000 genotypes

0 0.20 0.40 0.60 0.80 1

Norm Theta

BFGL-NGS-119315

-0.20

0

0.20

0.40

0.60

0.80

1

1.20

1.40

1.60

1.80

Norm

R

79 346 472

52,156 call rate >= 0.99

41,264 minor allele >= 0.1

31,466 minor allele >= 0.2

WGA vs. WGS

Bonferroni correction 1.03 e-6 = LOD 6.0

WGS?

• Using a large panel of markers to estimate genetic merit (MBV) marker breeding value.

• Using the marker information from across the whole-genome to estimate the sum of effects.

• How - Uses foundation information for the estimation process derived from training data sets (Equations).

• Animals are genotyped that may or may not have phenotypic information and genetic merit is estimated.

WGS – Whole-Genome Selection

• “There is no doubt that whole genome-enabled selection has the potential for being the most revolutionary technology since artificial insemination and performance-based index selection to change the nature of livestock improvement in the foreseeable future”

Dorrian Garrick, Iowa State University

AccurateMulti-traitSelection

Future Genetic Improvement of Beef Cattle?

U.S. Beef Cattle Grandsires USMARCGranddams

F1 Parents

Phenotypes

SNPGenotypes

Gene Flow

3rd Generation Progeny

Information Flow

Release the Data- Breed Association

• The results of the DNA tests will be critical in the National Sire Evaluations in the future.

• To estimate genetic effects for a trait all the data needs to be used (“good and bad alleles”).

• Selective reporting is a long-term disadvantage.

Players ChangingGenetic Visions (WI)

Infigen (WI)

Celera AgGen (California, Maryland)

Frontier Beef Systems

Genaissance

Genmark

Pyxis

ImmGen

Geneseek

Viagen

Identigen

Pfizer (Bovigen- Catapult)

Igenity SCR

MMI Genmark

Maxxam Genetic Solutions

Using DNA in Selection Programs

• Just because animal is not carrying the favorable allele for a specific test does not mean the animal is not genetically superior for the trait.

• Increase the accuracy of EPD

• Hard (expensive) traits to measure

• Sex-limited traits

• Lowly heritable traits

• Speed selection decisions

• Merchandising genetics

Tools for Selection

• - Growth

• - Feed efficiency

• - Carcass composition - quality

• - Reproduction

• - Disease resistance

Will markers replace “traditional” selection?

Marker-Assisted Selection (MAS)

- At the seed stock/multiplier level

Marker-Assisted Management (MAM)

- At the commercial level Seed Stock MAS

Commercial Cow/calfMAM & MAS

Finishing Phase MAM

Implementation to the Industry

Marker data- added to the databases to contribute to our

national genetic evaluation system already in place

Additional tool to be used in making genetic progress

2000 – Industry sires

Feed efficiency EBV through WGS

Where has animal genetic improvement lagged the most?

Animal Health - all species

BRD – Bovine Respiratory Disease

• Most costly disease to the cattle industry– 97.6% of feedlots treat – 14.4% of cattle are treated for symptoms– Accounts for over 50% of feedlot deaths

– Cattle treated for BRD are expected to return at least $40 less than untreated calves

NAHMS, 1999

Fulton et al., 2002

Reproduction - Beef, Dairy cattle

Low Heritability

Multi-component Trait

Where has animal genetic improvement lagged the most?

Ovulate one/two eggs

Fertilization None Open Cow

Pregnancy

Twins/singles Embryonic/fetal death

Dystocia Live Calves Open Cow

Death Survival

Weaning

Survives to Endpoint

• Lifetime productivity – all species– Longevity of female production makes the system more

profitable and is more environmentally friendly

– Female production efficiency

Where has animal genetic improvement lagged the most?

Sows – 3.6 paritiesSows – 3.6 paritiesDairy cows –– 2.8-3.2 parities

• Feed Efficiency – ?

Where has animal genetic improvement lagged the most?

Cattle Fax Issue 25, Vol. 40 June 20, 2008

Hard to measure traitExpensive!

• Little effort has been focused on the amount or causes of individual variation in efficiency of energy utilization by cattle, even though differences among individuals have long been recognized. (Johnson et al., 2002). USMARC & CSU

Dry matter intake x RFI

RFIkg/day

Day DMIkg

n=1032

Where has animal genetic improvement lagged the most?

Stage of production - Diet x Genetic interaction

GrowingCow production Finishing

Avoid single-trait selection

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0 0.005 0.01 0.015 0.02 0.025

HerefordSimmental

Daily Dry Matter Intake/ unit cow weightDaily Dry Matter Intake/ unit cow weight

Dai

ly H

eat

Pro

du

ctio

n, M

cal/

un

it w

eigh

tD

aily

Hea

t P

rod

uct

ion

, Mca

l/u

nit

wei

ght

Heat Production of Mature Hereford and Simmental Cows Pooled

Over Physiological States Fed at Varying Dry Matter Intakes Heat Production of Mature Hereford and Simmental Cows Pooled

Over Physiological States Fed at Varying Dry Matter Intakes

Matching genetic potential to the Climatic Environment

Ability of animals to adapt to various environments“Adaptability”

Where has animal genetic improvement lagged the most?

Where has animal genetic improvement lagged the most?

“On the radar” may become important

Treatment x Genetic InteractionImplants & feed additives (muscle enhancement)Health interventions (antibiotics)

Healthfulness of ProductOmega3 FA content of protein products

Managing breed composition

When breed composition is unknown

Using DNA in Selection Programs

• Just because the animal is not carrying the favorable allele for a specific test does not mean the animal is not genetically superior for the trait.

• Increase the accuracy of EPDs

• Hard (expensive) traits to measure

• Sex-limited traits

• Lowly heritable traits

• Speed selection decisions

• Merchandising genetics

Present/Future

• Will DNA testing play a role in the future of beef cattle- yes

Parental ID, Quantitative tests (panels), Simple genetic inheritance, WGS

• Will implementation be tough- Maybe; Yes(implementation of EPDs 80s, acceptance of crossbreeding programs, ultrasound)

• Collect tissues for DNA analysis Populations with phenotypes

• Breed Association responsibilities database, education

• Build database structure and become pro-active in the implementation of the new technology

But... another valuable tool for the breeder’s tool box

Marbling

B WtW WtY Wt

Tenderness

Mature Wt

Repro

Known Disease

Feed Intake

Udder/teat Feet/legs

Vision

• Larger panels of markers that explain greater portions of the genetic variation for traits. MAS MAM

• WGS - ?

Example - BW EPD 1.2 acc .75 on yearling bull– Validation, implementation

• Change in costs? – Technology driven

Questions