Concept of Genomic Selection
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Transcript of Concept of Genomic Selection
PRESENTED BY:
Kush Shrivastava
PRESENTED BY:
Kush Shrivastava
CONCEPT OF GENOMIC SELECTION:
A NEW BREEDING PARADIGM
Traditional breeding methods have been quite successful so far, without the knowledge of the genes acting on quantitative traits.Traditional breeding methods have been quite successful so far, without the knowledge of the genes acting on quantitative traits.
Breeders have enhanced production traits in their herds by selecting superior individuals as progenitors for the next generations.Breeders have enhanced production traits in their herds by selecting superior individuals as progenitors for the next generations.
Conventional selection – based on individual records, pedigree Conventional selection – based on individual records, pedigree or progeny performance or family performance.or progeny performance or family performance.
BREEDING VALUE
GENES ?
DNA DNA MarkersMarkers
Direct Genetic Marker
GENES
MARKER
A B
MARKERS
GENES
Indirect Genetic Marker
Phenotype
Estimated Breeding Value
Selection
Molecular Genetic MarkersEnvironment
Individual observatio
n
Information from
relatives
Genes
QTL
Requires prior knowledge of gene alleles or Requires prior knowledge of gene alleles or
markers that are associated with the traits markers that are associated with the traits
of interest.of interest.
Explains only a limited part of the genetic Explains only a limited part of the genetic
differences between individuals.differences between individuals.
Require families !Require families !
Whole genome SNPs are available for major Whole genome SNPs are available for major
livestock species.livestock species.
Whole genome genetic markers Whole genome genetic markers
Segregation of entire genome (and not merely Segregation of entire genome (and not merely
a set of specific regions of interest) can be a set of specific regions of interest) can be
followed.followed.
Paternal relationships not required !Paternal relationships not required !
MARKERS
Genomic selection was first described by Genomic selection was first described by
Meuwissen Meuwissen et alet al. (2001). (2001)..
Large number of SNPs are required.Large number of SNPs are required.
Distributed throughout the genome.Distributed throughout the genome.
Some might be close to region of interest.Some might be close to region of interest.
Can be used to explain variation in trait(s).Can be used to explain variation in trait(s).
Select on the basis of SNP effects of entire Select on the basis of SNP effects of entire
genome.genome.
(Eggen, 2012)(Eggen, 2012)
REFERENCE POPULATIONREFERENCE POPULATION
GENOTYPING
GENOTYPING
(Hayes (Hayes et alet al., 2009, 2013)., 2009, 2013)
PHENOTYPING
PHENOTYPING
PREDICTION EQUATION BASED ON SNP
PREDICTION EQUATION BASED ON SNP
W1X1 + W2X2 + W3X3 + W4X4…..
W1X1 + W2X2 + W3X3 + W4X4…..MOST OF THE GENETIC VARIATION
IN CAPTUREDMOST OF THE GENETIC VARIATION
IN CAPTURED
SELECTION ON CANDIDATES BASED ON GEBV ONLY
SELECTION ON CANDIDATES BASED ON GEBV ONLY
SUBSEQUENT GENERATIONS
Case I: MAS – consider a single SNP marker with Case I: MAS – consider a single SNP marker with
allele A & B allele A & B
Allele A: +4 AND Allele B: - 4Allele A: +4 AND Allele B: - 4
Animal no. Marker (SNP) allele
Value MAS- EBV
1 AA +8 +8
2 AB 0 0
3 BB - 8 - 8
Case II: Genomic selection - Consider 4 SNPs ; allele A vs. Case II: Genomic selection - Consider 4 SNPs ; allele A vs.
BB
SNP 1 – Allele A: +4 AND Allele B: -4SNP 1 – Allele A: +4 AND Allele B: -4
SNP 2 – Allele A: +2 AND Allele B: -2SNP 2 – Allele A: +2 AND Allele B: -2
SNP 3 – Allele A: +1 AND Allele B: -1SNP 3 – Allele A: +1 AND Allele B: -1
SNP 4 – Allele A: -3 AND Allele B: +3SNP 4 – Allele A: -3 AND Allele B: +3
(Eggen, 2012)
Genomic selection builds on existing Genomic selection builds on existing
breeding programs.breeding programs.
Better selection accuracy while reducing Better selection accuracy while reducing
the generation interval.the generation interval.
In cattle, more than 15 countries are now In cattle, more than 15 countries are now
using genomic breeding values.using genomic breeding values.
Schaeffer (2006)Schaeffer (2006) showed that using genomic selection, the showed that using genomic selection, the
genetic gain per year could be doubled in dairy cattle.genetic gain per year could be doubled in dairy cattle.
Fig. Timeline of a traditional artificial insemination breeding program based on progeny Fig. Timeline of a traditional artificial insemination breeding program based on progeny testing. EBV = estimated breeding value.testing. EBV = estimated breeding value.
0 yrs0 yrs
Bull A is born and is
selected based on
EBV
Bull A is born and is
selected based on
EBV
1 yrs 3mo1 yrs 3mo
Bull A is put to
progeny testing
Bull A is put to
progeny testing
2 yr2 yr
Progeny of Bull A is
born
Progeny of Bull A is
born
4 yr4 yr
Progeny of Bull A calves
Progeny of Bull A calves
4 yr 6 mo
4 yr 6 mo
Milk production
data of progeny of
Bull A become
available.
Milk production
data of progeny of
Bull A become
available.
EBV of bull A is estimated using progeny performance & can be
used as sire
EBV of bull A is estimated using progeny performance & can be
used as sire
5 yr 3 mo
5 yr 3 mo
Sons of Bull A are
born
Sons of Bull A are
born
Generation Interval = 63monthsGeneration Interval = 63months
(Schefers and Weigel, 2012)
Fig. Timeline of an aggressive artificial insemination breeding program based on the use of Fig. Timeline of an aggressive artificial insemination breeding program based on the use of genomic bulls as sires of sons. GEBV = genomic estimated breeding value; EBV = estimated genomic bulls as sires of sons. GEBV = genomic estimated breeding value; EBV = estimated breeding value.breeding value.
0 yrs0 yrs
Bull B is born &
selected on basis on
GEBV
Bull B is born &
selected on basis on
GEBV
1 yrs1 yrs
Bull B reaches sexual
maturity & can be used as
sire
Bull B reaches sexual
maturity & can be used as
sire
1 yr 9 mo
1 yr 9 mo
Progeny of Bull B is
born
Progeny of Bull B is
born
2 yr 9 mo
2 yr 9 mo
Progeny of Bull B
reaches sexual
maturity and can be
used as sire of sons.
Progeny of Bull B
reaches sexual
maturity and can be
used as sire of sons.
3 yr 6 mo
3 yr 6 mo
Grandson of Bull B are born
Grandson of Bull B are born
Grandson of Bull B reaches sexual maturity and can be used as sire of
sons
Grandson of Bull B reaches sexual maturity and can be used as sire of
sons
4 yr 6 mo
4 yr 6 mo
Milk production data from progeny of bull B are available
for calculation
of EBV
Milk production data from progeny of bull B are available
for calculation
of EBV
Generation Interval = 21 months
Generation Interval = 21 months
(Schefers and Weigel, 2012)
5 yr 3 mo
5 yr 3 mo
Great grandson of Bull B are born
Great grandson of Bull B are born
Started since 2008 in Started since 2008 in United States, Canada, New United States, Canada, New
Zealand, France, Netherlands.Zealand, France, Netherlands.
Genomic predictions values were more accurate Genomic predictions values were more accurate
than traditional pedigree index, specially for low than traditional pedigree index, specially for low
for low heritability traitsfor low heritability traits
Reduction of breeding bulls due to higher Reduction of breeding bulls due to higher
selection intensity in NZ. selection intensity in NZ.
(Van Raden et al. 2009)
(Spelman et al. 2010)
Genomic selection has not yet been implemented Genomic selection has not yet been implemented
in small ruminants.in small ruminants.
Pilot genomic evaluations have been implemented Pilot genomic evaluations have been implemented
in New Zealand.in New Zealand.
SNP chip still not available for goats.SNP chip still not available for goats.
However, due to limitation in current data However, due to limitation in current data
recording schemes - GS might produce inferior recording schemes - GS might produce inferior
results.results. (Van der Werf, 2009)
Pig breeding – Still under research & experimentation.Pig breeding – Still under research & experimentation.
SNP chips for pig is available.SNP chips for pig is available.
GI is low in pigs, therefore, to increase response, GS will be GI is low in pigs, therefore, to increase response, GS will be
effective for traits with low accuracy of selection (e.g., low effective for traits with low accuracy of selection (e.g., low
heritable traits, slaughter traits etc.)heritable traits, slaughter traits etc.)
Population size for low heritable traits – Approx. 10 times larger. Population size for low heritable traits – Approx. 10 times larger.
Increase of 68% in accuracy of the breeding values of the Increase of 68% in accuracy of the breeding values of the
experimental population over traditional selection.experimental population over traditional selection.
(Ibañez-Escriche and Gonzalez-Recio., 2011)
(Goddard, 2009)
(Forni et al. 2010)
(Ibañez-Escriche and Gonzalez-Recio., 2011)
Sequenced genome available in 2004.Sequenced genome available in 2004.
Three SNP panels of sizes 6, 12 and 42 K are Three SNP panels of sizes 6, 12 and 42 K are
available.available.
Experimental GS results are available.Experimental GS results are available.
Why should commercial breeder takes up GS ?Why should commercial breeder takes up GS ?
Cost effectiveness yet to be verified !Cost effectiveness yet to be verified !
(Hiller et al., 2004)
(Long et al. 2007; González-Recio et al. 2008 ,2009)
(Ibañez-Escriche and Gonzalez-Recio., 2011)
Sequencing data of almost all important livestock Sequencing data of almost all important livestock
species is available.species is available.
(Fan et al., 2010; Eggen, 2012)
1HD = High Density; LD = Low Density
2 Illumina Inc., San Diego, CA; Affymetrix, Santa Clara, CA.
(Eggen, 2012)
??Low Density = Low Cost;
lesser genome coverage
High Density = More Cost;
more genome coverage
Habier Habier et al.et al. (2009) (2009) and and Weigel Weigel et alet al. (2009; 2010) . (2009; 2010)
confirmed small losses in accuracy on the genomic confirmed small losses in accuracy on the genomic
predictions on using LD chips.predictions on using LD chips.
Strategy I - Strategy I - Weigel Weigel et alet al. (2009). (2009), select only based on , select only based on
SNPs that show strong association thus might be SNPs that show strong association thus might be
neglecting small effects.neglecting small effects.
2242
SNP
60000
SNPTrait
Goddard and Hayes (2008)Goddard and Hayes (2008) : Ancestors are genotyped with dense : Ancestors are genotyped with dense
panels and the selection candidates are genotyped with standard panels and the selection candidates are genotyped with standard
low-density panelslow-density panels
But it require pedigree, not a big deal for dairy cattle in developed But it require pedigree, not a big deal for dairy cattle in developed
nations.nations.
Success depends on 2 factors Success depends on 2 factors (Goddard, 2009)(Goddard, 2009): :
Size of the founder population
Size of the founder population
Choice of the ancestor
Choice of the ancestor
11 22 33 44
Reference populationReference population
700000 SNPs
700000 SNPs
2000 SNPs2000 SNPs
(Weigel et al., 2010)
High Density SNP map
High Density SNP map
Weigel Weigel et al.et al., (2010) – Utilizing genomic information from ancestor or relatives, , (2010) – Utilizing genomic information from ancestor or relatives,
there is only 4 % reduction in estimated breeding value by using LD chips as there is only 4 % reduction in estimated breeding value by using LD chips as
compared to HD chips.compared to HD chips.
Re- estimation of GEBV (after approx.7 generation) by using current population
as reference
Re- estimation of GEBV (after approx.7 generation) by using current population
as reference
Planned mating – Based on genetic relationshipPlanned mating – Based on genetic relationship
(Buch et al. 2012 )
High density SNP chips – Up to 7 lakh SNPsHigh density SNP chips – Up to 7 lakh SNPs
Whole genome sequencing & re- sequencing.Whole genome sequencing & re- sequencing.
Sequence based selectionSequence based selection
Inclusion of sequence dataInclusion of sequence data
40 % gain in accuracy of predicted breeding value40 % gain in accuracy of predicted breeding value
(Meuwissen and Goddard, 2010)
Accuracy maintained up to 10 generations
Accuracy maintained up to 10 generations
Livestock have large numbers of symbionts, Livestock have large numbers of symbionts,
(bacteria and protozoa) in digestive tract.(bacteria and protozoa) in digestive tract.
Affect some key traits – feed conversion efficiency.Affect some key traits – feed conversion efficiency.
Rumen microbiome profile is important – methane Rumen microbiome profile is important – methane
production.production.
Beef cattle – more methane production.Beef cattle – more methane production.
Emission depends upon type of ruminal microbes Emission depends upon type of ruminal microbes (Johnson and Johnson, 1995)
Microbes carried by an animal depend on features of the Microbes carried by an animal depend on features of the
animal (to some extent).animal (to some extent).
Can be regarded as part of the phenotype of the animal and Can be regarded as part of the phenotype of the animal and
subject to genetic variation.subject to genetic variation.
Bensen Bensen et alet al., 2010; Identified QTL in mice affecting gut ., 2010; Identified QTL in mice affecting gut
mircoflora.mircoflora.
Selection of cattle for “desirable” gut microflora.Selection of cattle for “desirable” gut microflora.Look for genome wide SNPs that favour the desirable gut ecosystemLook for genome wide SNPs that favour the desirable gut ecosystem
Manipulation of microbes be external effects (like feed etc.)Manipulation of microbes be external effects (like feed etc.)
(Hayes et al., 2013)
Treated genome as a “Black Box”Treated genome as a “Black Box”
Not necessary to know what's Not necessary to know what's
INSIDE it.INSIDE it.
No “credit” for function of individual No “credit” for function of individual
gene.gene.GENES !!GENES !!
Phenotype
E
Markers
Function of each gene(& the variation within)
Understanding of the biology of the animal that makes phenotypic variations significant
OP
EN
I
T
OP
EN
I
T
(Eggen, 2012)
Limited by the absence of programs that record phenotypes on Limited by the absence of programs that record phenotypes on
pedigreed animals pedigreed animals
lack of evaluation or national testing programs to assess the lack of evaluation or national testing programs to assess the
genetic value of germplasm.genetic value of germplasm.
Genomic approaches should help in identifying critical populations Genomic approaches should help in identifying critical populations
for preservation for preservation
Conservation of some local well-adapted breeds.Conservation of some local well-adapted breeds.
Genomics = manage what we canGenomics = manage what we can measuremeasure
Collecting a minimum number of phenotypes in the field is one of Collecting a minimum number of phenotypes in the field is one of
the critical and challenging steps to further deployment of genomic the critical and challenging steps to further deployment of genomic
selection in developing countries.selection in developing countries.(Eggen, 2012)