Arendonk, J.A.M., van

Bouwman, A.C.

Bovenhuis, H.

Visker, M.H.P.W.

English

Name not available

Genome-Wide Association Of The Ratio Of Saturated To Unsaturated Milk Fatty Acids In Dutch Dairy Cattle

Wageningen Yield 

  
Genome-Wide Association Of The Ratio Of Saturated To Unsaturated 
Milk Fatty Acids In Dutch Dairy Cattle 
A.C. Bouwman*, H. Bovenhuis, M.H.P.W. Visker and J.A.M. van Arendonk 
Introduction 
Milk fat is an important substance of the human diet; it provides energy, fat-soluble nutrients 
(including essential nutrients) and bioactive lipids (German and Dillard, 2006). However, the 
consumption of saturated fatty acids (SFA) is associated with increased risk of cardio-
vascular diseases, while unsaturated fatty acids (UFA) are neutral or even seen to decrease 
this risk (Jensen, 2002; Mensink et al., 2003). Dairy products have high amounts of SFA, 
especially C14:0 and C16:0, and relatively low amounts of (poly)UFA (Maijala, 2000; 
Mensink et al., 2003).  
Stoop et al. (2008) as well as Soyeurt et al. (2008) showed that ratio SFA/UFA has a 
heritability of 0.20 and 0.27, respectively. The identification of genomic regions and 
individual genes responsible for this variation will enhance the understanding of biological 
pathways involved in FA synthesis and offers further opportunities to improve milk fat 
composition through breeding. 
Recent developments in molecular genetics have made it possible to perform genome wide 
association studies using thousands of single nucleotide polymorphism (SNP) markers to 
detect quantitative trait loci (QTL). Schennink et al. (2009) performed genome wide linkage 
analysis for long-chain fatty acids, including ratio SFA/UFA. Using a denser marker map 
with linkage analysis provides little extra information about the position of a QTL, unless the 
number of recombinants is increased by increasing the population size (Darvasi et al., 1993). 
Genome wide association using a denser marker map has a higher power to detect QTL with 
a small effect on the trait and results in more precise locations. The aim of this study was to 
use a dense marker map to confirm previously detected QTL for ratio SFA/UFA in the 
linkage analysis by Schennink et al. (2009) and to position them to a more precise location, 
and to possibly identify additional QTL associated with ratio SFA/UFA.  
Material and methods  
Data. For this study phenotypic data was recorded from 1,905 first-lactation Dutch Holstein 
Friesian cows. Milk fat composition was measured by gas chromatography. More detailed 
information about the data and the gas chromatography can be found in Stoop et al. (2008). 
Individual SFA (C4:0-C20:0), mono-UFA (C10:1-C18:1), and poly-UFA (C18:2-C18:3) 
were measured. The fatty acids were expressed as weight-proportion of total fat weight. 
Ratio SFA/UFA was calculated as the sum of SFA divided by the sum of UFA. The cows 
were genotyped using a custom Infinium Assay (Illumina, USA). This approach resulted in 
50,856 technically successful SNPs. Monomorphic SNPs, SNPs with a genotyping rate 
smaller than 80% and SNPs with a genotype frequency of less than 10 animals for one of the 
genotypes were discarded from the analysis resulting in 43,516 SNPs. 
                                                 
*
 Animal Breeding and Genomics Centre, Wageningen University, .P.O. Box 338, 6700 AH Wageningen, The 
Netherlands 
  
Statistical analysis. A two-step single SNP association analysis was performed for ratio 
SFA/UFA. The first step involved a fast screen of the whole genome using a general linear 
model. In this first step the R package SNPassoc (González et al., 2007) was used. The 
phenotypes analyzed were pre-corrected for four systematic environmental effects: days in 
milk, age at first calving, calving season and herd. The correction factors were estimated on 
the phenotypes of all 1,905 cows using an animal model in ASReml: 
ijklmnnmlk
2
j4j3
dim*05.0
2i1ijklmn eAherdscodeseasonafc*bafc*be*bdim*by i +++++++++µ= − (1) 
where y was the (uncorrected) phenotype; µ was the overall mean; dim was the covariate 
describing the effect of days in lactation; afc was the covariate describing the effect of age at 
first calving; season was the fixed effect of the class of calving season (June-Aug ‘04, Sept-
Nov ’04, or Dec ‘04-Jan ‘05); scode was the fixed effect accounting for differences in 
genetic level between groups of proven bull daughters and young bull daughters; herd was 
the random effect of herd; A was the random additive genetic effect of the individual; and e 
was the random residual. For the SNPassoc analysis only the 1,706 individuals which had 
both phenotypic and genotypic information were used. Sire effect was included in the 
SNPassoc model to account for half sib-relations. The general linear model used was:  
ijji
*
ij eSNPsirey +++µ= ,            (2) 
where y* was the phenotype adjusted for the systematic environmental effects; sire was the 
fixed effect of sire; SNP was the fixed effect of SNP genotype; and e was the random 
residual. The genome-wise false discovery rate (FDR) was controlled according to the 
method of Storey et al. (2003), by calculating q-values based on the P values for each 
marker. Associations with a genome-wise q-value smaller than 0.05 were called significant.  
In the second step regions with significant SNP effects were further analyzed using an 
animal model in ASReml (Gilmour et al., 2006), to account for all family relationships. For 
this analysis model (1) was extended with a SNP effect.   
Results and discussion 
A genome wide association study of ratio SFA/UFA was performed using 43,516 SNPs 
(figure 1). The SNPassoc analysis detected 209 genome-wise significant SNPs which were 
located on BTA 2, 3, 4, 5, 9, 12, 14, 19, 24 and 26. Associations on BTA 3, 5, 14 and 19 
remained significant when accounting for all family relationships using model 2. The two 
most significant regions were BTA 14 and BTA 19.  
The most significant SNP associated with ratio SFA/UFA was located at 0.4 Mbp on BTA 
14 and explained 38.3% of the genetic variation of ratio SFA/UFA (table 1). This QTL 
confirms the genome-wise significant QTL detected by Schennink et al. (2009) at 0 cM for 
ratio SFA/UFA in their linkage study with 1,341 SNPs, on the same population, but with half 
the number of animals. The two most significant SNPs on BTA 14 were the two SNPs of the 
dinucleotide substitution of acylCoA:diacylglycerol acyltransferase (DGAT1) causing a K to 
A amino acid substitution, which has an effect on milk fat (Grisart et al., 2002) and milk fat 
composition, including ratio SFA/UFA (Schennink et al., 2007).  
On BTA 19 the most significant SNP associated with ratio SFA/UFA was located at 37.3 
Mbp and explained 5.3% of the genetic variation of ratio SFA/UFA (table 1). Previous 
studies have reported QTL for several milk fatty acids between 51 and 98 cM on BTA 19, 
but not for ratio SFA/UFA (Morris et al., 2007; Schennink et al., 2009; Stoop et al., 2009).  
  
 
Figure 1: Manhattan plot for the association of 43,516 SNPs with ratio SFA/UFA. The 
dashed line corresponds to a threshold level of FDR = 0.05. All –log10(P values) > 7 are 
not shown  
 
On BTA 19 there are many genes related 
to fat metabolism, such as sterol regulatory 
element-binding protein 1 (SREBP1), 
signal transducer and activator of 
transcription 5A (STAT5A), ATP citrate 
lyase (ACLY), growth hormone (GH) and 
fatty acid synthase (FASN). Therefore, this 
region was of further interest.  
The most significant SNP on BTA 19 was 
located, together with 2 other significant 
SNPs, in the calcium channel, voltage-
dependent, T type, alpha 1G subunit 
(CACNA1G). A SNP in the GH gene also 
showed a very significant association with 
ratio SFA/UFA. The SNP in the FASN 
gene showed no significant effect. None of 
the SNPs investigated were located in the 
other before mentioned genes related to fat 
metabolism.  
All 1,293 SNPs on BTA 19 were re-analyzed in ASReml. The R2 of the regression of the 
test-statistic of the SNPassoc analysis on the test-statistics of the animal model for all SNPs 
on BTA 19 was 0.86 (figure 2). This indicates that the SNPassoc analysis including a 
correction for sire gave similar results as the ASReml analysis accounting for all family 
relations. Therefore, SNPassoc can be considered as a good method for the determination of 
interesting regions. Of course the animal model in ASReml is a better model, but such an 
analysis would take too much time to run for many thousands SNPs.  
 
These results show that there are markers significantly associated with ratio SFA/UFA, 
which can be exploited for selection in breeding programs. The genome wide association 
study also results in more precise locations, giving better opportunities to indicate candidate 
genes.  
y = 1.0406x + 0.0102
R² = 0.859
0
1
2
3
4
5
6
0 1 2 3 4 5 6
S
N
P
a
s
s
o
c
ASReml
 
Figure 2: Regression plot of –log10(P 
values) from the SNPassoc and ASReml 
analysis for all 1,293 SNPs on BTA 19 
 
  
Table 1: Most significantly associated SNP (according to the SNPassoc analysis) per 
chromosome, their q-value, the –log10(P value) of the SNPassoc and ASReml analysis 
and the percentage of the genetic variance explained by the SNP 
BTA SNP Name                               Position q-value  -Log10(P value) Var1 
   (Mbp)  SNPassoc ASReml (%)  
2 ULGR_BTA-101688 71.4 0.026 3.99 3.40  3.9 
3 ULGR_BTA-122118 125.2 0.019 4.16 4.06 4.7 
4 ULGR_rs29009595 87.5 0.035 3.81 2.21 2.8 
5 ULGR_AAFC03122217_7089 99.9 0.009 4.50 3.89 5.0 
9 ULGR_rs29013915 36.7 0.016 4.23 3.35 4.0 
12 ARS-BFGL-NGS-82501 37.1 0.034 3.82 2.42 3.1 
14 ULGR_SNP_AJ318490_1c 0.4 1.6E-26 30.10 30.61  38.3 
19 ULGR_BTA-45363 37.3 0.001 5.71 4.42 5.3 
24 ARS-BFGL-NGS-6396 56.5 0.032 3.85 3.56  2.1 
26 ULGR_BTA-60969 24.6 0.029 3.93 3.35 3.9 
1
 the percentage of the genetic variance explained by the SNP 
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Darvasi, A., Weinreb, A., Minke, V. et al. (1993). Genetics 134:943-951. 
German, J.B., and Dillard, C.J. (2006). Crit. Rev. Food Sci. Nutr. 46:57-92. 
Gilmour, A.R., Gogel, B.J., Cullis, B.R. et al.  (2006). ASReml User Guide Release 2.0 
VSN International Ltd, Hemel Hempstead, UK. 
Grisart, B., Coppieters, W., Farnir, F. et al. (2002). Genome Res. 12:222-23. 
Jensen, R.G. (2002). J. Dairy Sci. 85:295-350. 
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Schennink, A., Stoop, W.M., Visker, M.H.P.W. et al. (2009). J. Dairy Sci. 92:4676-4682.  
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Genome-Wide Association Of The Ratio Of Saturated To Unsaturated Milk Fatty Acids In Dutch Dairy Cattle

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