Low-Cost, Low-Density Genotyping and its Potential Applications

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Low-Cost, Low-Density Genotyping and its
Potential Applications
K.A. Weigel, O. González-Recio, G. de los Campos,
H. Naya, N. Long, D. Gianola, and G.J.M. Rosa
University of Wisconsin
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Illumina BovineSNP50 Genotyping BeadChip
lt 250 per animal today
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Low-Cost Genotyping Assays

• ? At the current price, the BovineSNP50 BeadChip
  is limited to applications involving males and
  elite females
• A low-cost assay with 300-1000 SNPs might
  deliver a substantial portion of the gain for a
  small fraction of the price
• ? Applications may include preliminary screening
  of young bulls, selection of replacement heifers,
  genomic mating programs, and parentage discovery

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Which SNPs to Select?
Pick the SNPs with largest estimated effects?
How many do we need?
VanRaden, 2008
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Which SNPs to Select?
Pick evenly spaced SNPs?
How many do we need?
VanRaden, 2008
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Entropy

• A measure of the impurity of an arbitrary
  collection of examples (S)
• Entropy (S) - p log2p - p- log2p-
• where
• p proportion of positive examples in S
• p- proportion of negative examples in S

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Information Gain

• A measure of the effectiveness of an attribute
  in classifying the data
• Reduction in entropy caused by partitioning the
  examples into subsets (S1,...,Sn) based on values
  of a given attribute (A)
• Information Gain (S,A)
• Entropy(S) ?i1,n (Si/S) Entropy(Si)

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Top 10 of SNPs for Net Merit (Info Gain for 20
highest bulls vs. 20 lowest bulls)
3252 SNPs
1181 SNPs (36.2) in common (though many more in
linkage disequilibrium)
Number of SNPs
Chromosome
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Effects of Top Net Merit SNPs
3252 SNPs
Estimate
Chromosome
3252 SNPs
Estimate
Chromosome
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Top 10 of SNPs for Specific Traits (Info Gain
for 20 highest bulls vs. 20 lowest bulls
traditional coding)
3252 SNPs
Number of SNPs
3252 SNPs
Chromosome
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Top 2.5 of SNPs for Specific Traits (Info Gain
for 20 highest bulls vs. 20 lowest bulls
traditional coding)
813 SNPs
Number of SNPs
813 SNPs
Chromosome
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Top Info Gain SNPs in Common by Trait (20
highest vs. 20 lowest bulls traditional coding)
Milk Fat Prot PL SCS DPR NM
Milk 901 2265 340 439 468 1600
Fat 114 1044 341 445 319 726
Prot 484 158 342 587 490 1634
PL 21 24 18 272 1056 952
SCS 46 32 64 7 283 270
DPR 56 29 52 159 20 843
NM 274 81 276 149 12 117
top 10 of SNPs (3252) above the diagonal
top 2.5 of SNPs (813) below the diagonal
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Bayesian LASSO

• Bayesian least absolute selection and shrinkage
  operator
• One-step method for estimating effects of
  important SNPs while shrinking estimates for
  unimportant SNPs towards zero
• Assumes SNP effects follow a double exponential
  distribution (a few with large effects, many with
  negligible effects)

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Distribution of SNP Effects (analysis of Net
Merit in training set with 32,518 SNPs)
Number of SNPs
Estimated SNP Effect (genetic SD)
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Distribution of SNP Effects (analysis of Net
Merit in training set with 32,518 SNPs)
Estimated Effect (genetic SD)
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Distribution of SNP Effects
Mean SD Min. Max
300 SNPs 0.0031 0.0540 -0.1749 0.1474
500 SNPs -0.0008 0.0436 -0.1078 0.1317
750 SNPs -0.0002 0.0372 -0.0912 0.1084
1000 SNPs -0.0011 0.0321 -0.1094 0.1006
1250 SNPs -0.0017 0.0286 -0.1022 0.0818
1500 SNPs -0.0014 0.0259 -0.1090 0.0850
2000 SNPs -0.0009 0.0229 -0.0898 0.0900
32,518 SNPs -0.0001 0.0030 -0.0405 0.0230
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Validation of Genomic PTAs

• Compute parent averages and genomic PTAs using
  2003 data from 3,305 Holstein bulls born in
  1952-1998
• ? Training Set
• Compare ability to predict daughter deviations
  in 2008 data for 1,398 bulls born from 1999-2002
• ? Testing Set

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Predictive Ability for Net Merit(Genomic PTA
vs. Progeny Test PTA in Testing Set)
Corr. 0.61
PTA from Progeny Testing (SD)
32,518 SNPs
Predicted Genomic PTA from All SNPs (gen. SD)
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Predictive Ability for Net Merit(Genomic PTA
from SNPs vs. Progeny Test PTA in Testing Set)
Corr. 0.43
Corr. 0.52
750 SNPs
300 SNPs
PTA from Progeny Testing
Corr. 0.55
Corr. 0.57
2000 SNPs
1250 SNPs
Predicted Genomic PTA from Top ___ SNPs (gen. SD)
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Predictive Ability for Net Merit(Genomic PTA
vs. Progeny Test PTA in Testing Set)
Predictive Ability in Testing Set
. . .
Number of SNPs used for Prediction
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No. Bulls Chosen Correctly (of 1399)
Top 50 (700 bulls) Top 25 (350 bulls) Top 10 (140 bulls) Top 5 (70 bulls) Top 2½ (35 bulls) Top 1 (14 bulls)
300 SNPs 460 (65.7) 161 (46.0) 31 (22.1) 13 (18.6) 3 (8.6) 0 (0.0)
500 SNPs 460 (65.7) 180 (48.6) 39 (27.9) 12 (17.1) 3 (8.6) 1 (7.1)
750 SNPs 479 (68.4) 180 (48.6) 39 (27.9) 15 (21.4) 4 (11.4) 2 (14.3)
1000 SNPs 484 (69.1) 180 (48.6) 40 (28.6) 11 (15.7) 3 (8.6) 2 (14.3)
1250 SNPs 482 (68.9) 179 (51.1) 43 (30.7) 12 (17.1) 4 (11.3) 1 (7.1)
1500 SNPs 479 (68.4) 183 (52.2) 46 (32.9) 14 (20.0) 5 (14.3) 1 (7.1)
2000 SNPs 489 (69.9) 186 (53.1) 42 (30.0) 17 (24.3) 6 (17.1) 2 (14.3)
32,518 SNPs 499 (71.3) 191 (54.6) 49 (35.0) 16 (22.9) 5 (14.3) 2 (14.3)
Note that we are predicting 2008 PTAs that have
REL much less than 99 (not the true genetic
merit of the bulls)
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Animal ID Applications(96 SNPs in the
parentage panel)

• ? Verify reported parents
• ? Discover parents if unknown or incorrect
• ? Trace animals or animal products

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Effects on Inbreeding

• ? Traditional animal model evaluations favor
  co-selection of families or relatives
• ? Genomic selection allows within-family
  selection, which leads to less inbreeding
• ? Low-cost, low-density genotyping assays will
  allow widespread screening of families that might
  provide unique genetic contributions to the
  population
• ? Identification and control of inherited defects
  will be greatly enhanced as well

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Potential for Mate Selection

• ? Millions of cows are mated using computerized
  programs each year, based on faults in
  conformation or avoidance of inbreeding
• ? SNP genotypes of AI sires and potential mates
  could be used to minimize inbreeding or to
  identify parents with complementary DNA
  profiles

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Possibilities for Novel Traits
? Opportunities to collect DNA and phenotypes for
traits not routinely assessed in national
recording schemes ? Examples include feed
intake, hormone level, immune function, hoof
care, etc. ? Potential resource populations
include experimental herds, calf ranches, heifer
growers, commercial herds with specific
milking/feeding/management equipment, veterinary
databases (without sire ID)
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Novel Traits and Genomics
Recorded Population (10,000-25,000 animals per
trait or trait group)
additive or non-additive inheritance
no selection bias
refine estimates of location or effect, add SNPs
update estimates of SNP effects
full genotyping
selective genotyping
Whole Genome Selection
QTL Detection and MAS
200/genotype 100/trait 5 traits/group select
high/low 10
cost 7.0-17.5 mln per trait group
cost 1.2-3.0 mln per trait
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Synergy with Herd Management

• ? Personalized medicine is the Holy Grail of
  biomedical research
• Examples include genotype-guided Warfarin dosing
  using two major genes
• Cost-effective applications in livestock will
  involve a series of small returns from enhanced
  vaccination programs, ration formulation, mate
  selection, veterinary care, and animal grouping
  decisions
• ? Integration with herd management software will
  be the key to success

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in Research, Extension and Instruction
http//www.wisc.edu/dysci
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