Potential for Genomic Selection in indigenous breeds and results of GWAS in Gir dairy cattle of gujrat

Potential for Genomic Selection
in Indigenous Cattle Breeds
POSTGRADUATE INSTITUTE OF VET. EDU. & RESEARCH,
KAMDHENU UNIVERSITY, GANDHINAGAR, GUJRAT
Dr. P. H. Vataliya
Dr. Nilesh Nayee
Dr. Rajeshkumar Thakkar

FLOW OF PRESENTATION…..
 Introduction
 Pre Genomic Era
 Genomic Selection
 SNPs
 Methodology of Genomic
Selection
 Comparison of methods of
Genomic Selection
 How does Genomic Selection
alter Selection ?
 Factors affecting the Accuracy
of Genomic Selection
 Impact of Genomic Selection in
Animal Breeding
 Advantages of Genomic Selection
 Limitations of Genomic Selection
 Genomic Selection around the
world
 Genomic Selection in INDIA
 Conclusions
 Future Prospects

 The main goal in animal breeding is to select individuals having high
breeding values for traits of interest as parents to produce the next generation
as quickly as possible.
 Most programs rely on statistical analysis of large databases with phenotypes
on breeding populations by linear mixed model to estimate breeding values.
 Use of genetic markers to identify QTLs and their use in marker-assisted
selection.
 The advent of high-density SNP genotyping, combined with novel statistical
methods for the use of data to estimate breeding values, has resulted in
application of genomic selection in dairy cattle.
INTRODUCTION

 Selection
i. Differential rate of reproduction
ii. Choosing parents of Next Generation
 A form of MAS using genetic marker covering whole
genome
genomic selection is not an exact science but it is a
powerful new tool for predicting results based on SNPs
INTRODUCTION

(Intensity of Selection • Accuracy of Selection •Genetic Standard Deviation )
∆G = ---------------------------------------------------------------------------------
Generation interval
 ∆G depends primarily upon the ability to recognize genotypes with superior
breeding values
 Generation interval inversely related to genetic gain
 Thus, assessment of breeding value of all individuals comprising a population
is required
INTRODUCTION

 Individual’s breeding values assessed on the basis of
Own performance
Performance of collateral relatives
Pedigree information
Performance of progeny
 The progeny testing has a HIGH ACCURACY OF
SELECTION, among the methods described above, but the
GENERATION INTERVAL is also HIGH
INTRODUCTION

 Works well if:
– Heritability of traits is medium to high
– Progeny’s performance data is available
 Less effective if:
– Heritability of traits is low or sex limited traits
– Animals are young (No progeny and thus no records are
available)
Traditional Breeding

MOLECULAR GENETICS: Studies the structure and function
of gene at a molecular level
QTL DETECTION AND MAPPING: Quantitative trait
loci(QTLs) are scattered genes over the genome and they are
responsible for inheritance of quantitative trait
MAS STRATEGIES: Marker assisted selection (MAS)
is indirect selection process where a trait of interest is
selected, not based on the trait itself, but on a marker linked to
it
GENOMICS: Study of genomes of individuals
GENOMIC ERA

 MAS explains only a limited proportion of the total genetic
variance captured by the markers
 An alternative to tracing a limited number of QTL with markers is
to trace all the QTL
 This can be done by dividing the entire genome up into
Chromosomal Segments.
PRE GENOMIC ERA

 Selection based on the prediction of breeding values from the
information of dense markers covering the whole genome
 GS is MAS on a genome-wide scale
 By using Genome Wide-dense markers : use Total Genetic
Variance (Vg)
 Traditional MAS : ~10% of Total Genetic Variance (Vg)
(Meuwissen et al. 2001)
GENOMIC SELECTION

 Genomic selection exploits linkage disequilibrium
 The assumption is that the effects of the chromosome segments
will be the same across the population because the markers are in
LD with the QTL that they bracket
(Hastbacka et al., 1992)
 Hence the marker density must be sufficiently high to ensure that
all QTL are in LD with a marker or haplotype of markers
Cont...
GENOMIC SELECTION

1. A large number of progeny tested bulls (or recorded
females preferably with known pedigree) and their
DNA samples
2. Genotyping these samples for a large number of
Polymorphic SNPs
3. Appropriate statistical methods and models for estimating
GBVs
REQUIREMENT FOR GENOMIC SELECTION

 There are different types of markers available for MAS, like
RFLP, AFLP, Microsatellite, Minisatellite and SNPs.
 RFLP, AFLP, Microsatellite and Minisatellite are not evenly
spread over genome compare to SNPs
 SNPs are single nucleotide variation in DNA sequence of
Genome differs between member of population.
SNPs

 Now a days Genomic Selection is
based on DNA chip which are of
SNPs.
 This kind of chips are called as SNP
chip or Snip chip.
 Sequencing of livestock genomes
has discovered 100s of 1000s of
Single Nucleotide Polymorphism
(SNP) markers
Cont...
SNPs

Why SNPs ?
 More frequent in the genome
 Polymorphic
 Low mutation rate
 Work well across laboratories
 Already available as part of genomic selection breeding
programs
SNPs

Marker located relatively closer to genes of interest
SNPs

(Illumina)
Micro-array bead chips for various species released till now…..
SNPs

SNPs are most valuable for traits that are
 Of low heritability
 Difficult or expensive to measure (feed efficiency)
 Unmeasurable until after selection has occurred (carcass data)
 Currently not selected due to lack of available phenotypic data
(tenderness)
 Sex limited traits (milk production)
(Van Eenennaam, 2011)
18
SNPs

 Part of the population is genotyped using the dense SNP chip and
phenotyped for quantitative traits. This is referred as the
Reference Population
 The establishment of an appropriate Reference Population is one
of the key aspects in Genomic Selection
METHODOLOGY OF GS

 For dairy cattle Reference Populations, Saatchi et al, (2010)
recommended to use reliable (>90%) progeny tested sires from
recent generations rather than older bulls.
 Size of the Reference Population is inversely proportional to the
heritability of the trait and directly proportional to the effective
population size (Goddard, 2009).
METHODOLOGY OF GS

 The effect of all the SNPs is estimated in the Reference
Population by statistical models; where the association between
SNPs and phenotypes is calculated. For working out the
prediction equations
 The rest of the population (other then Reference Population) is
genotyped using the same SNP chip and the total genetic value
(GEBV) of the animals is predicted by using the prediction
equations derived from Reference Population
(Meuwissen et al., 2001)
Cont..
METHODOLOGY OF GS

Methods for estimation of GEBV
 Least Squares Analysis (LS)
 Genome-wide Best Linear Unbiased Prediction (GW-BLUP)
 Bayesian approach
 Least Squares: Test all the genes one by one for their statistical
significance, and set the effects of the non-significant genes to zero,
while estimating the effects of the significant genes simultaneously
using LS.
(Meuwissen et al., 2001)
METHODOLOGY OF GS

Cont...
 Best Linear Unbiased Prediction: Fit the allelic effects as
random effects instead of as fixed effects. The fitting of
random effects does not require degrees of freedom, and
thus all allelic effects can be estimated simultaneously.
 Bayesian estimation: This is similar to BLUP, except that
the variance of the allelic effects is assumed different for
every gene, and is estimated by using a prior distribution
for this variance.
METHODOLOGY OF GS

Accuracy
 Meuwissen et al., (2001) reported Accuracy of Genomic
Selection by using various statistical models.
Method Accuracy
LS .36
BLUP .74
Bayes .84
COMPARISON OF METHODS

On the basis of Marker density
 Low – little differences between GW-BLUP and Bayesian
methods, accuracy low to moderate
 High – saturation of efficiency of GW-BLUP, whereas Bayesian
approaches increase in accuracy
(Boichard, 1996)

Reliabilities
 Reliability GEBVs of Bayes only 1% higher than GW-BLUP
 Reliability of Bayes is substantially higher for traits influenced
by large QTL
(Van Raden et al, 2008)

 Population structure/history
• Size of sib families
• Generation interval
 Availability of dense marker maps
 Availability of many genotyped individuals with records
USEFULNESS OF GS DEPENDS ON:

Based on marker approach
 Comparing the accuracy of genomic selection with – Haplotypes (IBD
/ IBS approach)
IBD- Identical by descent i.e. two genes have originated from
replication of one single gene in a previous generation
IBS- Identical by state i.e. two genes may not have originated from
replication of same gene but still they have functional identity
(Falconer,1996)
ACCURACY OF GS

Calus et al. (2007) used simulated data
Based on marker approach
ACCURACY OF GS

 Reliabilities of SNP markers of various densities were
reported by VanRaden (2010) as follows:
SNP density Reliabilities
3k 70% - 80%
50k 83%
700k 84%
Based on marker density
ACCURACY OF GS

 Linkage disequilibrium between QTL and markers = density of
markers
 In dairy cattle populations, an average r2 of 0.2 between
adjacent markers is only achieved when markers are spaced
every 100kb
Based on LD
ACCURACY OF GS

 Number of records used to estimate chromosome
segment effects
 Meuwissen et al. (2001) evaluated accuracy using LS,
BLUP, Bayes using 500, 1000 or 2000 records in the
reference population
No. of phenotypic records
500 1000 2000
Least squares 0.124 0.204 0.318
BLUP 0.579 0.659 0.732
Bayes 0.708 0.787 0.848
Based on No of records
ACCURACY OF GS

(Goddard and Hayes. 2009)
Based on No of records
ACCURACY OF GS

How often to re-estimate GEBVs?
 Meuwissen et al. (2001) reported the correlation between Estimated
BV(EBV) and True BV(TBV) in generations 3rd -8th
Denser markers; require more generations between re-estimation of GEBV (Meuwissen
et al., 2001)
Generation
3rd 0.848
4th 0.804
5th 0.768
6th 0.758
7th 0.734
8th 0.718
RE-ESTIMATION OF GEBVs

Dairy Cattle 60-120% (Pryce et al., 2011)
Meat sheep 21% (Van der Werf, 2011)
Wool sheep 38% (Van der Werf, 2011)
Layers 40% (Dekkers et al., 2009)
Broilers 20% (Dekkers et al., 2009)
ESTIMATIED INCREASED ∆G From GS

Selection of young bulls (no progeny test)
Saves costs of progeny test: ~ 40,000 $/bull
Reduces generation interval by factor 2
Annual savings of $23 million (92%)
Condition: Updated marker information are available
(Schaeffer, 2006)
IMPACT OF GS ON DAIRY CATTLE BREEDING

 Once marker effects are estimated they can be useful for a few
generations
 Selection possible on novel traits, which otherwise require expensive
phenotyping
 New breeding strategies can be implemented
 Increased genetic gain
– By increasing accuracy of selection
– By reducing the generation interval due to early selection
 Select animals before they are of productive and/or reproductive age
 Eliminate the need for progeny testing
ADVANTAGE OF GS

 Genotyping still costly
 Marker estimates must be estimated in population that they will be used in
 If generation intervals are shortened substantially then annual inbreeding
rates could be higher
 Need to genotype a sufficiently large set of animals for accurate marker
estimates
 For traits with lower heritability - more records needed
LIMITATION OF GS

 ADHIS produced genomic based breeding values for bulls in
September and December 2010.
 2,381 Holstein bulls were included in the September 2010
analysis (2,193 reference bulls and 188 young bulls). In this
group of 188 young bulls with almost no daughter performance
data, an improvement in reliability across all key traits is
evident.
(Hayes et al, 2009)
GLOBAL SCENARIO OF GS

 GEBVs obtained by USDA in collaboration with Canada for
Holstein bulls have been released in public every year since 2008
 A project at Guelph with 820 bulls was carried out with the
following results
Traits
Reliability –
Traditional
methods
Reliability –
Genomic
Selection
Protein yield 38 46
Fat yield 38 43
SCS 30 48
Conformation 39 47
(Pryce and Daetwyler, 2012)
GLOBAL SCENARIO OF GS- CANADA

High Ranking Genomic Young Bulls

 CRV launched InSire bulls – Designated to GS selected bulls, since
2008, for Holstein and Jersey breeds
 Increases in the reliability using GEBVs were also reported by CRV
as follows :
Traits Additional reliability
Protein production 17%
Overall Conformation 14%
SCS 11%
(Hayes et al, 2008)
GLOBAL SCENARIO OF GS- NATHERLAND(CRV)

 LIC had the foresight to store DNA from every sire that was
progeny tested since 1980.
 This enabled LIC to genotype sires that were the best, and the
worst too, of their progeny test cohort and thus evaluate markers
across the genetic range.
 The degree of accuracy of GEBVs was measured by their
correlation with Progeny test BVs and were found to be ranging
from 0.45 to 0.60 for production traits in HF breed
(Hayes et al, 2008)
GLOBAL SCENARIO OF GS- NATHERLAND(LIC)

Nordic countries (VikingGenetics)
 VikingGenetics got the first genomic indexes of Holstein, Jersey and
Red Breeds during the later parts of year 2007
 They have genomic indexes every two months for purposes of
purchase of new bulls to their breeding programme
(Pryce and Daetwyler, 2012)
GLOBAL SCENARIO OF GS

 Policy makers need to be convinced of the potential of effective
breeding programmes
 Extensive validation of association between genotypes and
phenotypes needed
 GS can be use if accurate performance records are available
 Only genotyping without phenotyping and efficient data analysis
will be wasteful expenditure
GS IN INDIA

Genome wide association study of
udder type traits and 305 day milk
yield in Gir cattle

Genome Wide Association Study of udder type traits
and 305 day milk yield in Gir cattle
 Total 559 animals were available for genotyping
 Animals were genotyped using the customized Illumina Bovine HD microarray chip
assay INDUSCHIP 1
 The 45,024 SNPs were distributed over all 29 Bos indicus autosomes
 Certain chromosomes having large numbers of SNPs affecting a trait
 9 udder type traits recommended by ICAR, investigated for genome wide association
study
 A total of 838 genome-wise significant SNPs associated with 9 udder type traits (p-
value < 0.0001) were found

Trait wise no. of significant SNPs
Sr. No. Traits Number of significant SNPs
1 Fore udder attachment 11
2 Central ligament 06
3 Fore teat placement 03
4 Rear teat placement 07
5 Rear udder height 07
6 Rear udder width 38
7 Teat length 166
8 Udder depth 14
9 Teat thickness 89
10 305day milk yield 497
Total 838

Chromosome wise no. of SNPs per Trait
CHR No. FUA CL FTPL RTPL RUH RUW TL UD TTH MILK Total
1 2 3 4 5 6 7 8 9 10 11 12
1 0 0 0 0 0 1 8 0 2 19 30
2 1 0 0 0 0 1 3 0 6 19 30
3 2 0 0 0 1 1 6 0 1 18 29
4 0 0 0 2 2 0 3 0 2 15 24
5 1 2 0 0 0 2 31 1 17 59 113
6 1 0 2 0 0 2 10 5 6 25 51
7 0 0 0 1 0 1 9 0 3 17 31
8 1 0 0 0 0 0 3 0 2 12 18
9 0 0 0 0 0 8 14 0 1 28 51
10 1 0 0 0 0 2 3 0 2 15 23
11 1 1 0 0 0 1 7 0 4 17 31
12 0 0 0 1 0 0 5 0 3 13 22
13 0 0 0 0 0 1 6 0 3 15 25
14 0 0 0 0 0 4 10 2 5 27 48
15 0 0 0 1 1 1 1 1 1 9 15
16 1 0 0 0 0 0 6 0 4 23 34
17 0 0 0 0 0 4 7 1 5 21 38
18 0 0 0 1 0 0 0 0 2 11 14
19 0 0 0 1 0 2 3 2 6 14 28
20 1 0 0 0 0 2 0 0 1 20 24
21 0 0 1 0 1 2 0 0 4 24 32
22 0 0 0 0 0 1 9 0 0 12 22
23 0 1 0 0 0 0 5 0 3 14 23
1 2 3 4 5 6 7 8 9 10 11 12
24 0 0 0 0 0 1 4 2 0 8 15
25 0 0 0 0 1 0 5 0 2 11 19
26 0 0 0 0 0 0 2 0 1 13 16
27 0 1 0 0 1 1 2 0 2 5 12
28 0 0 0 0 0 0 3 0 0 11 14
29 1 1 0 0 0 0 1 0 1 2 6
Total 11 6 3 7 7 38 166 14 89 497 838

 Fore udder attachment
 A total 11 SNPs were found to be associated significantly (p-value <0.0001)
 Maximum numbers of SNPs were located on chromosome No. 3 (2 SNPs) in this region
between base pair position 85.5MB to 100.8MB.
Genome Wide Association Study

 Central ligament
 Maximum no. of SNPs were on chromosome No. 5 (2 SNPs: NDDB-BOVINEHD0500024877
and NDDB-BOVINEHD0500026042) in this region between base pair position 87.7MB to
91.8MB.

 Fore teat placement
 Maximum no. of SNPs were located on chromosome No. 6 (2 SNPs: BTB-01530236 and
NDDB-BOVINEHD0600006154) in this region between base pair position 22.30MB to
22.32MB

 Rear teat placement
 Maximum no. of SNPs were located on chromosome No. 4 (2 SNPs) in this region between
base pair position 1.57MB to 21.8MB

 Rear udder height

 Rear udder width

 Teat length

 Udder depth

 Teat thickness

 305day milk yield

 The GWAS of these important traits with the SNP chip array i.e.
INDUSCHIP1 can be the footstep to be embark upon the option of
Genomic Selection in Gir cattle
 The Genomic Selection thus incorporated into the Cattle Breeding
Programme would ensure rapid genetic progress of Gir, an important
indigenous dairy cattle of India

GENOME WIDE ASSOCIATION STUDY FOR
BODY CONFORMATION TRAITS AND
305 DAY MILK YIELD

• The 45024 SNPs included in the INDUSCHIP 1 SNP chip were distributed over all 29 Bos
indicus autosomes.
• Majority of SNPs were (26,007 out of 45,024) spread under MAF class 0.3-0.5, while, only
3883 SNPs were observed under MAF class 0.1-0.2.
• A total 2009 genome-wise significant SNPs associated with 10 conformation traits and
milk yield (p-value < 0.0001) were found by PLINK software

2941
4415
4112
39483946
3539
28642893
2788
2286
2594
2003
1827
17071710
1439
12
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
No.ofSNPs
Chromosome No.
Histogram of chromosome wise no. of SNPs
10682
3883
4452
9890
16117
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
0-0.1 0.1-0.2 0.2-0.3 0.3-0.4 0.4-0.5
No.ofSNPs
Minor Allele Frequency (MAF)
Histogram of MAF wise no. of SNPs

Manhattans plot of Stature
• A total 632 SNPs were found to be genome wise significant (p-value < 0.0001) for stature in the present study
• The chromosome with the highest number of associated SNPs was chromosome 5 which had 64 significantly associated
SNPs in this region between base pair position 1 Mb to 155 Mb.

Manhattans plot of Body depth
• A total 148 genome-wise significant SNPs associated with body depth (p-value < 0.0001) were found in the present study.
• The chromosome with the highest number of associated SNPs was chromosome 5 which had 23 significantly associated SNPs in this region
between base pair position 3 Mb to 130 Mb.

Body length
• A total 209 SNPs were found to be genome wise significant (p-value < 0.0001) for body length in present study.
• The chromosome with the highest number of associated SNP was chromosome 6 which had 22 significantly associated SNPs in this region
between base pair position 1 Mb to 125 Mb.

Manhattans plot of Heart girth
• A total of 451 genome-wise significant SNPs associated with heart girth (p-value < 0.0001) were found in present study
• The chromosome with the highest number of associated SNPs was chromosome 5 which had 46 significantly associated SNPs in
this region between base pair position 1 Mb to 155 Mb.

Manhattans plot of Angularity
A total 11 SNPs were found to be genome wise significant (p-value < 0.0001) for angularity in
present study

Manhattans plot of Rump angle
• A total of 14 genome-wise significant SNPs associated with rump angle (p-value < 0.0001) were found in
present study
• The highest number of 5 significantly associated SNPs were found with chromosome 1.

Manhattans plot of Rump width
• A total 30 SNPs were found to be genome wise significant (p-value < 0.0001) for rump width in present study
• The chromosome with the highest number of associated SNPs was chromosome 9 which had 6 significantly associated
SNPs in this region between base pair position 1 Mb to 117 Mb.

Manhattans plot of Rear leg side view
• Genome-wise 7 significant SNPs associated with rear leg set were found in present study
• The significant SNPs associated with rear leg were observed on chromosome 1, 3, 5 and 24.

Manhattans plot of Rear leg rear view
• A total 5 SNPs were found to be genome wise significant (filter p-value < 0.0001) for rear leg rear view in present study
• The significant SNPs associated with rear leg rear view were observed on chromosome 1, 5, 15, 21 and 25

Manhattans plot of Foot angle
• Genome-wise significant SNPs associated with foot angle were 5 in the present study
• The significant SNPs associated with foot angle were observed on chromosome 6, 8, 10 and 11.

Manhattans plot of 305day Milk yield
• A total of 497 genome-wise significant SNPs associated with milk yield (with filter p-value < 0.0001)
were found by genome-wide association study
• The chromosome with the highest number of associated SNPs was chromosome 5 which had 59
significantly associated SNPs in this region between base pair position 1Mb to 118 Mb.

CONCLUSIONS FROM GWAS
 All the conformation traits in Gir animals were intermediate except rear leg rear view. Most of the
conformation traits were desirable, however, some traits showed the undesirable characteristics
viz. heart girth, body depth, body length, stature and foot angle. So, such traits needed to
improvement.
 The body depth, heart girth, body length and stature were highly positively and significantly
correlated with 305day milk yield. This indicated that the deeper, longer and taller cows might
produce more milk than the narrower and shorter cows.
 The phenotypic correlation of body depth and heart girth, body length, stature were highly
correlated and positive highly significant (p<0.01)

POSSIBILITIES IN INDIA
 Large Non Descript animals need to be graded up with High
Genetic Merit Sires of known breeds.
 Establishment of well organized breeding network,
performance recording system and reference population
 Initially globally available 50k SNP chip may be tried in
indigenous breeds
 INAPH recording system of NDDB is coming up which can
be used as performance records for Genomic Selection

CONCLUSIONS
 Dairy industry uniquely suited for genomic selection
 GS in dairy cattle is need of day for obtaining faster
genetic gain (High Accuracy)
 Several countries are implementing genomic selection
 Hybrid systems merging classic and genomic selection
are arising
 Usefulness of GS depends on population structure,
availability of dense marker maps and large reference
population

FUTURE PROSPECTUS….
 Genotyping of more SNP to get clearer ‘picture’ of
genetic variation
 Genotyping and get records for more animals
 Refinement and development of new estimation methods
 Accurate performance recording and reliable record
keeping as well as precise genotyping initiatives will be
required for developing nations like India

THANK YOU
genomic selection is not an exact
science but it is a powerful new tool for
predicting results !

Potential for Genomic Selection in indigenous breeds and results of GWAS in Gir dairy cattle of gujrat

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