Download Role of Genomics in Selection of Beef Cattle for Healthfulness

Role of Genomics in
Selection of Beef Cattle for
Healthfulness Characteristics
Dorian Garrick
[email protected]
Iowa State University &
National Beef Cattle Evaluation Consortium
Agenda
 Selection and Prediction
 Breeding Values
 Breeding Objective
 Conventional Prediction of Breeding Values
 Genomic Prediction of Breeding Values
 Concept and Theory
 Application and Practice
 Present Status for Healthfulness Characteristics
 Knowledge Gaps (and Research Needs)
Selection and Prediction
 Genetic change results from using candidates that differ
from population average as parents of the next
generation
 The key to genetic change is selection – more intense
selection will provide faster change (1-2% pa)
 The key to artificial selection is quantifying the breeding
merit for attributes of interest
 This is achieved using breeding values
Breeding Values
 A breeding value is twice the deviation in performance
of the offspring relative to offspring of average parents
 adjusted for the merit of the mates
 adjusted for non-genetic influences on performance (eg
age at measurement)
Breeding Objectives
If you’re not
breeding for profit,
we wish you well
with your hobby
Breeding Objectives
 Two components
 First, the list of traits that influence income &or costs
 These are the traits for which we need breeding values
 Second, the relative emphasis of each trait in the list
 Value of a unit change in that trait, all other traits held
constant
$INDEX=r1BV1+r2BV2+…….rnBVn
Breeding Objectives
If you’re not
breeding for profit,
we wish you well
with your hobby
Income that allows for profit in the beef
industry derives from consumer
satisfaction in the eating experience
Conventional Prediction
 We cannot observe Breeding Values (BV)
 We predict them and refer to the estimates as EBV
 Beef Industry uses its own jargon, a term known as
EPD, for Expected Progeny Difference
 EPD= ½ EBV
Suppose we get progeny on a bull
Sire
Progeny
Performance of the Progeny
+30 lb
+15 lb
-10 lb
Sire
Offspring of one sire exhibit
more than ¾ diversity of
the entire population
+ 5 lb
+10 lb
Progeny +10 lb
We learn about parents from progeny
+30 lb
+15 lb
-10 lb
+ 5 lb
Sire
+10 lb
(EPD is “shrunk”)
Sire EPD +8-9 lb
Progeny +10 lb
EPDs on widely-used (old) sires
are accurate
With enough progeny,
this is usually close to
the bulls true EPD
Sire
Sire EPD +8-9 lb
EPD on offspring are parent average
EPD +9
EPD +1
GV +
EPD +5
+5
+5
+5
+5
+5
+5
+5
+5
+5
Current Circumstances
EPD +9
EPD +1
GV +
EPD +5
True +1
+5
+20
+5
+0
+5
-5
+5
-10
+5
+20
+5
-5
+5 +5
-25 +50
+5
+5
Current Circumstances
EPD +9
EPD +1
GV +
EPD +5
True +1
+5
+20
+5
+0
+5
-5
+5
-10
+5
+20
+5
-5
+5 +5
-25 +50
But identifying those better than parent average requires phenotypes
+5
+5
But genes determine the EPD
Part of 1 pair
of chromosomes
From sire
From dam
Cattle usually have 30 pairs of chromosomes
One member of each pair was inherited from the sire, one from the dam
Each chromosome has about 100 million base pairs (A, G, T or C)
Blue base pairs represent genes
Yellow represents markers inherited from the sire
Orange represents markers inherited from the dam
Definition of Breeding Value
 BV is sum of average effects of alleles, summed over
the pair of alleles at each locus and over all loci
influencing the trait
Mendelian Viewpoint
EPD is half sum of the gene effects
+3
-3
-6
+6
+5
+5
Sum=+2
Sum=+8
Blue base pairs represent genes
The EPD is half the sum of all these genetic values
(half because offspring inherit a random half sample
of each parents chromosomes)
But what is the genomic architecture of various traits?
EPD is half sum of the gene effects
+3
-6
+5
-3
+6
+5
If we knew the number, location
and effect of the genes,
we could obtain EPD directly,
before the bull was breeding age
Gene Assisted Selection (GAS)
Qq
EPD +9
EPD +1 Q q
Suppose Q is +5 better than q
Sort by: Q Q
Qq
qq
Gene Assisted Selection (GAS)
Qq
EPD +9
EPD +1 Q q
Suppose Q is +5 better than q
+10
+10
Sort by: Q Q
+5
+5
+5
Qq
+5
+5
0
0
qq
0
Genomic Prediction
 Involves finding the location and effects of the genes
(known as QTL=Quantitative Trait Loci) that cause
variation in the trait of interest in a discovery phase
 Then using this information to determine the genetic
merit of a new individual that need not be recorded
itself for the phenotype of interest
Accurate EPD (eg from AI sires)
Regress EPD on QTL
genotype
Variation due to
other genes
Slope=average effect of allele
qq
Qq
QQ
December 2004
February 2001
April 2009
Genotypes are now a commodity !
EPD or phenotype
Practice – regress EPD on
SNP
Use SNP genotypes at locus 1 (in high LD) as surrogates for QTL
A1A1
A1B1
B1B1
True Breeding Value
Practice – BV on SNP
Use SNP genotypes at locus 2 (in low LD) as surrogates for QTL
A2A2
A2B2
B2B2
Linkage Disequilibrium on Bos Taurus autosome 1
LD indicates the ability of observed SNP to act as surrogates
(of other SNP)
Hope this reflects the LD between SNP and QTL
1,000 mixed breeds half-sib groups
Birth Weight on BTA6 in 3,500
Angus bulls
Major Fatty Acids
(Proportions in Phospholipids)
Informative 1cM Regions
Cross Validation
Training
 Partition the dataset (by sire) into say three groups
G1
G2
✓
Derive g-EPD
G3
Validation
✓
G1
Compute the
correlation between
predicted genetic
merit from g-EPD and
observed performance
Cross Validation
Training
 Every animal is in exactly one validation set
✓
G1
G2
✓
G3
✓
✓
G1
G2
Validation
✓
✓
G3
Fatty Acids are a System
Distribution of Joint Effects
C14:0 and C16:0 (1cM windows)
Knowledge Gaps
 What are the key healthfulness targets?
 These dictate the list of traits to predict
 From which depots? (Longissimus dorsi, subcutaneous etc)
 What is their relative importance?
 To each other and to productive/reproductive traits
 How can we more cheaply phenotype them?
 Important for validation and beef marketing
 Can we construct a small panel of (causal?) SNP that operate across
breed?
 Only practically deliverable through a genomics option
 Can a value chain be created that can take this science from the
computer to the consumer?
Summary
 Science is showing considerable promise in being able
to predict genetic merit so that the concentration of
healthfulness traits can be increased or decreased by
directed selection
 We need more clarity on the targets
 We need more fine-mapping studies and validation of
these targets – expression array work would help
 We need an entrepreneurial framework to deliver the
prototype concept in meaningful quantities to test the
market potential
Acknowledgments
 Pfizer Animal Genetics funded the healthfulness study
 Drs Jim Reecy and JR Tait managed the overall study and
laboratory data collection at ISU
 Drs Raluca Mateescu and Deb VanOverbeke at Oklahoma
State, and Alison Van Eenennaam at UC Davis managed
data collection in some of the cohorts
 Dr Clara Diaz, INIA Madrid provided the results of the
genomic analyses of the fatty acids