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

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Information Gain. Top 10% of SNPs for Net Merit ... milking/feeding/management equipment, veterinary databases (without sire ID) ... – PowerPoint PPT presentation

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


1
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
2
Illumina BovineSNP50 Genotyping BeadChip
lt 250 per animal today
3
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

4
Which SNPs to Select?
Pick the SNPs with largest estimated effects?
How many do we need?
VanRaden, 2008
5
Which SNPs to Select?
Pick evenly spaced SNPs?
How many do we need?
VanRaden, 2008
6
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

7
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)

8
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
9
Effects of Top Net Merit SNPs
3252 SNPs
Estimate
Chromosome
3252 SNPs
Estimate
Chromosome
10
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
11
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
12
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
13
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)

14
Distribution of SNP Effects (analysis of Net
Merit in training set with 32,518 SNPs)
Number of SNPs
Estimated SNP Effect (genetic SD)
15
Distribution of SNP Effects (analysis of Net
Merit in training set with 32,518 SNPs)
Estimated Effect (genetic SD)
16
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
17
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

18
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)
19
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)
20
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
21
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)
22
Animal ID Applications(96 SNPs in the
parentage panel)
  • ? Verify reported parents
  • ? Discover parents if unknown or incorrect
  • ? Trace animals or animal products

23
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

24
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

25
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)
26
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
27
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

28
UW-Madison Dairy ScienceCommitted to Excellence
in Research, Extension and Instruction
http//www.wisc.edu/dysci
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