Dairy cattle experiencing heat stress have reduced production, fertility, and health compared to non-heat stressed animals, and dairy cattle in the southern US commonly are heat-stressed for at least some of the year. The objective of this study was to perform a genome-wide association study (GWAS) for rectal temperature (RT) during heat stress in lactating Holstein cows to identify single nucleotide polymorphisms (SNP) associated with genes that have large effects on RT. The largest proportion of SNP variance (0.07 to 0.44%) was explained by markers flanking the region between 28,877,547 and 28,907,154 bp on Bos taurus autosome (BTA) 24. That region is flanked by U1 (28,822,883 to 28,823,043 bp) and NCAD (28,992,666 to 29,241,119 bp). In addition, the SNP at 58,500,249 bp on BTA 16 explained 0.08% and 0.11% of the SNP variance for 2- and 3-SNP analyses, respectively. That contig includes SNORA19, RFWD2, and SCARNA3. Other SNP associaed with RT were located on BTA 16 (close to CEP170 and PLD5), BTA 5 (near SLCO1C1 and PDE3A), BTA 4 (near KBTBD2 and LSM5), and BTA 26 (located in GOT1, a gene implicated in protection from cellular stress). There do appear to be QTL associated with RT in heat-stressed dairy cattle, but the QTL explain relatively small proportions of overall additive genetic variance. These SNP could prove useful in genetic selection and for identification of genes involved in physiological responses to heat stress.

1. Use of Genome-wide Association Mapping to Identify Chromosomal Regions
Associated with Rectal Temperature During Heat Stress in Holstein Cattle
John B. Cole*,1, Serdal Dikmen2, Daniel J. Null1, and Peter J. Hansen3
1Animal Improvement Programs Laboratory, Agricultural Research Service, USDA, Beltsville, MD; 2Department of Animal Science, Faculty of Veterinary Medicine, Uludag University, Bursa, Turkey; and
3Department of Animal Sciences, D.H. Barron Reproductive and Perinatal Biology Research Program, and Genetics Institute, University of Florida, Gainesville, FL 32611
• The authors thank the following dairies for
providing access to cows and records:
Alliance Dairy (Trenton, Florida), Hilltop
Dairy (Trenton, Florida), Larson Dairy
(Okeechobee, Florida), McArthur Dairy
(Okeechobee, Florida), North Florida
Holsteins (Bell, Florida) and the University
of Florida Dairy Unit (Hague, Florida).
• J.B. Cole and D.J. Null were supported by
appropriated project 1265-31000-096-00.
Dairy cattle experiencing heat stress have
reduced production, fertility, and health
compared to non-heat stressed animals, and
dairy cattle in the southern US commonly are
heat-stressed for at least some of the year. The
objective of this study was to perform a genome-
wide association study (GWAS) for rectal
temperature (RT) during heat stress in lactating
Holstein cows to identify single nucleotide
polymorphisms (SNP) associated with genes
that have large effects on RT. The largest
proportion of SNP variance (0.07 to 0.44%) was
explained by markers flanking the region
between 28,877,547 and 28,907,154 bp on Bos
taurus autosome (BTA) 24. That region is flanked
by U1 (28,822,883 to 28,823,043 bp) and NCAD
(28,992,666 to 29,241,119 bp). In addition, the
SNP at 58,500,249 bp on BTA 16 explained 0.08%
and 0.11% of the SNP variance for 2- and 3-SNP
analyses, respectively. That contig includes
SNORA19, RFWD2, and SCARNA3. Other SNP
associaed with RT were located on BTA 16 (close
to CEP170 and PLD5), BTA 5 (near SLCO1C1 and
PDE3A), BTA 4 (near KBTBD2 and LSM5), and
BTA 26 (located in GOT1, a gene implicated in
protection from cellular stress). There do appear
to be QTL associated with RT in heat-stressed
dairy cattle, but the QTL explain relatively small
proportions of overall additive genetic variance.
These SNP could prove useful in genetic
selection and for identification of genes involved
in physiological responses to heat stress.
Summary
References
Dikmen, S., J.B. Cole, D.J. Null, and P.J.
Hansen. 2013. Genome-wide association
mapping for identification of quantitative trait
loci for rectal temperature during heat stress
in Holstein cattle. PLoS ONE. 8(7):e69202.
Table 1. The 20 loci with the largest
proportion of SNP variance explained
using 3-SNP sliding windows.
Figure 1. Proportion of marker variance
explained (%) by 3-SNP sliding windows
Materials and Methods
• Phenotypes collected on 9 Florida dairies:
• 1500 to 1700 h from June to September 2007
• 1400 to 1600 h from June to September in
2010, 2011 and 2012
• RT measured under shade and, generally,
cows were in head locks (free-stall barns) or
resting (tunnel-ventilation barns)
• Data collected from days in which the THI at
the time of collection was > 78.2
• This indicates that heat stress was sufficient
to cause hyperthermia
Materials and Methods (cont’d)
• 1,451 animals (107 cows and 1,344 bulls) had
available genotypes using the Illumina
BovineSNP50 BeadChip (Illumina, Inc., San
Diego, CA, USA).
• Animals and SNP with call rates < 0.90, SNP
with minor allele frequencies < 0.05,
monomorphic SNP, and animals with parent-
progeny conflicts were dropped
• The final dataset included 39,759 SNP from
1,440 individuals
• A pedigree including 12,346 ancestors was
obtained from the National Dairy Database
(Beltsville, MD, USA)
• Single-step genomic BLUP (ssGBLUP) was
used to simultaneously estimate genomic
breeding values and allele substitution effects
• Fixed effects were parity, year, stage of
lactation, location of cows, farm type,
technician, and farm. THI and milk yield
were included as covariates
• Random effects were additive animal,
permanent environment, and random
residual error
• Results were calculated using a Bayes A
model as implemented in the BLUPF90
program modified for genomic analyses.
• GWAS for RT were conducted with
individual SNP effects, as well as moving
averages of 2, 3, 4, 5, and 10 consecutive
SNP, using the POSTGSF90 package
Key Results
• The 20 largest explanatory SNP for RT are
shown in Table 1. Allele substitution effects
are plotted in Figure 1 as the proportion of
variance explained.
• The largest proportion of variance was
explained by a region on BTA 24:
• The region is flanked by a U1 spliceosomal
RNA (U1) and a cadherin-2 (NCAD).
• U1 is involved in postranscriptional
modification and regulation of mRNA
length, which could be related to changes
in gene expression in cells exposed to
elevated temperature.
SNP name Chromosome Location (bp) Variance explained (%)
BTB-01646599 24 28941584 0.284289
ARS-BFGL-NGS-41140 24 28975828 0.258319
Hapmap58887-rs29013502 24 28907154 0.244098
BTB-00638221 16 35272426 0.169289
BTB-01485274 24 28877547 0.153510
ARS-BFGL-NGS-71584 26 20290497 0.150088
ARS-BFGL-NGS-23064 26 20365711 0.145223
BTB-01267098 5 89545151 0.118979
ARS-BFGL-NGS-89847 7 2457750 0.115361
BTA-26221-no-rs 28 35345760 0.109708
Hapmap46698-BTA-38760 16 35317388 0.107123
ARS-BFGL-NGS-108847 16 58500249 0.106275
ARS-BFGL-NGS-29516 23 14246801 0.103440
ARS-BFGL-NGS-35716 24 29013292 0.100621
ARS-BFGL-NGS-95833 26 37797893 0.098292
ARS-BFGL-NGS-16848 28 2924302 0.094916
BTA-27496-no-rs 12 2500836 0.092552
BTB-01267080 5 89512928 0.090345
ARS-BFGL-NGS-107395 29 47527067 0.089550
ARS-BFGL-NGS-100006 23 14215024 0.082668
1
• A SNP at 58,500,249 bp on BTA 16 is near
SNORA19, RFWD2, and SCARNA3.
• SNORA19 is involved in initiation of translation.
• SCARNA3 encodes a small nucleolar RNA
similar to SNORA19.
• RFWD12 encodes a protein ligase, that selects
proteins for proteasomal degradation.
• A consensus region of BTA 5 at ~89,500,000 bp is
flanked by solute carrier organic anion
transporter family member 1C1 (SLCO1C1) and a
phosphodiesterase (PDE3A).
• Human SLCO1C1 mediates the Na+-
independent high-affinity transport of thyroxine
and reverse triiodothyronine, and may be
involved in the mechanism which depresses
plasma thyroxine concentrations in heat-
stressed dairy cows.
Acknowledgments
• The QTL identified in this study may be
useful for genetic selection for
thermotolerance, although additional data
are needed to compute high-reliability
genetic predictions.
• Several candidate genes for regulation of RT
were identified, and one or more of them
may play an important role in physiological
adaptation to heat stress.
• One candidate gene, SLC01C1, which is
involved in regulation of metabolic rate
through transport of thyroxine, may play a
regulatory role in RT.
• More commonly, candidate genes play roles
important for stabilizing cellular function
during stress. Among these are GOT1,
which synthesizes the cytorotective
compound sulphur dioxide, genes involved
in protein ubiquitination (KBTBD2 and
RFWD12), and genes involved in RNA
metabolism (LSM5, SCARNA3, SNORA19,
and U1).
Conclusions