Genetic selection has been very successful when applied to traits of moderate to high heritability, but progress has been slow for traits with low heritabilities. The problem is further compounded when novel traits are considered because data needed to calculate high-reliability PTA generally are not available. A combination of producer-recorded health event data and SNP genotypes may permit the routine calculation of PTA with reasonable reliabilities for health traits. Poster presented at the 5th International Symposium on Animal Functional Genetics in Guaruja, Brazil, in 2014.

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John B. Cole1,*, Kristen L. Parker Gaddis2, Francesco Tiezzi2, John S. Clay3, and Christian Maltecca2
1Animal Improvement Programs Laboratory, Agricultural Research Service, USDA, Beltsville, MD, USA
2Department of Animal Science, College of Agriculture and Life Sciences, North Carolina State University, Raleigh, NC, USA
3Dairy Records Management Systems, Raleigh, NC, USA
*Corresponding author: john.cole@ars.usda.gov
Introduction
Genetic selection has been very successful when applied to
traits of moderate to high heritability, but progress has
been slow for traits with low heritabilities. The problem is
further compounded when novel traits are considered
because data needed to calculate high-reliability PTA
generally are not available. A combination of producer-
recorded health event data and SNP genotypes may permit
the routine calculation of PTA with reasonable reliabilities
for health traits.
Results and discussion
Multiple-trait genomic heritabilities ranged from 0.02
for lameness to 0.36 for retained placenta (Table 1).
Methods
Genomic evaluation of low-heritability traits: dairy cattle health as a model
Objectives
The objective of this study was to perform genomic analyses
on producer-recorded health data for disorders commonly
encountered by dairy cows in the US. Genome-wide
association studies were performed to identify regions of the
genome associated with these health problems.
Variance components and PTA were computed for several
health traits (cystic ovaries, displaced
abomasum, ketosis, lameness, mastitis, metritis, and
retained placenta). Traditional single- and multiple trait
models (st-BLUP and mt-BLUP) were fit using only pedigree
and phenotypic information. Pedigree, phenotypic, and
genomic data were combined in single- and multiple-trait
analyses using a single-step procedure (st-ssBLUP and mt-
ssBLUP). Models included fixed parity and year-season
effects, and random herd-year and sire effects.
Following data quality edits, the first-parity dataset
included 134,226 records from 12,893 sires and 13,534
maternal grandsires (MGS), and the later-parity dataset
had 174,069 records from parities two through five for
100,635 cows from 11,481 sires and 11,716 MGS.
Genotypes were available from 7,883 sires for 37,713 SNP.
Analyses used ASReml v3.0 (st-BLUP) or the BLUPF90
family of programs from the University of Georgia (all
others). st-ssBLUP results from the Georgia programs were
used for GWAS with single SNP or 1 Mbp windows.
Acknowledgments
Conclusions
Health traits were lowly heritable, making consistent, long-
term goals essential for genetic improvement.
Selection for fewer disease events will have a favorable
impact on fertility, longevity, and economic merit because
of desirable genetic correlations.
Individual SNP and sliding windows explained only small
proportions of genetic variance.
Figure 1 Manhattan plot for clinical mastitis using st-ssBLUP.
Single-SNP results
1 Mbp sliding-window results
Health Event Pedigree Information
Blended Pedigree & Genomic
Information
Overall
mean
Unproven
sires
1
Proven
sires
2
Overall
mean
Unproven
sires
Proven
sires
Overall
gain
3
Displaced abomasum 0.44 0.22 0.65 0.55 0.38 0.71 0.11
Ketosis 0.35 0.18 0.52 0.48 0.35 0.61 0.13
Lameness 0.24 0.15 0.32 0.39 0.31 0.47 0.15
Mastitis 0.39 0.26 0.52 0.51 0.40 0.61 0.12
Metritis 0.35 0.24 0.46 0.48 0.38 0.57 0.13
Retained placenta 0.55 0.42 0.67 0.64 0.54 0.73 0.09
1 1 Unproven sires considered sires with less than 10 daughters.
2 Proven sires considered sires with at least 10 daughters.
3 The increase in mean reliability calculated as the difference in overall mean reliability between
the blended model and the traditional (pedigree data only) model.
Table 2 Average reliabilities of sire PTA computed with pedigree information and
genomic information.
Table 1 Estimated heritabilities (SD) on the diagonal with estimated genetic
correlations below the diagonal from multiple-trait genomic-based analysis with first
parity records.
* Genetic correlations significant at P < 0.05.
Sire reliabilities increased on average by about 12% when
genomic data were included (Table 2).
The GWAS for clinical mastitis highlighted several genomic
regions that should be investigated (Figure 1).
Genetic correlations of health with fitness traits were
significant in most cases (Table 3).
* Genetic correlations significant at P < 0.05.
Table 3 Approximate genetic correlations (SE) between fitness traits and results from
pedigree-based analysis with first parity records .