Abe, T., Saburi, J., Hasebe, H., Nakagawa, T., Misumi, S.,
Nade, T., ... Kobayashi, E. (2009). Novel mutations of the
FASN gene and their effect on fatty acid composition in
Japanese Black beef. Biochemical Genetics, 47, 397-411.
https://doi.org/10.1007/s10528-009-9235-5
Arakawa, A., Iwaisaki, H., & Anada, K. (2009). Estimation of
breeding values from large-sized routine carcass data in
Japanese Black cattle using Bayesian analysis. Animal
10
Science Journal, 80, 617-623.
11
https://doi.org/10.1111/j.1740-0929.2009.00681.x
12
Asa, R., Okamoto, M., Sasaki, K., Ooi, M., Hagiya, K., &
13
Kuchida, K. (2017). Relationship between the fineness of
14
marbling and sensory evaluation in Japanese Black cattle.
15
Nihon Chikusan Gakkaiho, 88, 139-143. (In Japanese with
16
English summary) https://doi.org/10.2508/chikusan.88.139
17
Ashida, I., & Iwaisaki, H. (1999). An expression for average
18
information matrix for a mixed linear multi-component of
19
variance model and REML iteration equations. Animal
20
Science Journal, 70, 282-289.
21
https://doi.org/10.2508/chikusan.70.282
22
Corbin, C. H., O'Quinn, T. G., Garmyn, A. J., Legako, J. F.,
23
Hunt, M. R., Dinh, T. T. N., ... Miller, M. F. (2015).
24
Sensory evaluation of tender beef strip loin steaks of
25
varying marbling levels and quality treatments. Meat
26
Science, 100, 24-31.
19
https://doi.org/10.1016/j.meatsci.2014.09.009
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum
likelihood from incomplete data via the EM algorithm.
Journal of the Royal Statistical Society: Series B
(Methodological), 39, 1-22.
https://doi.org/10.1111/j.2517-6161.1977.tb01600.x
Dryden, F. D., & Marchello, J. A. (1970). Influence of total
lipid and fatty acid composition upon the palatability of
three bovine muscles. Journal of Animal Science, 31, 36-
10
11
41. https://doi.org/10.2527/jas1970.31136x
Honda, T., Ishida, T., Kobayashi, I., Oguri, Y., Mizuno, Y.,
12
Mannen, H., ... Oyama, K. (2016). Change of fatty acid
13
composition of the lumbar longissimus during the final
14
stage of fattening in the Japanese Black cattle. Animal
15
Science Journal, 87, 578-583.
16
https://doi.org/10.1111/asj.12443
17
Ibi, T., Kahi, A. K., & Hirooka, H. (2006). Effect of carcass
18
price fluctuations on genetic and economic evaluation of
19
carcass traits in Japanese Black cattle. Journal of Animal
20
Science, 84, 3204-3211. https://doi.org/10.2527/jas.2005-
21
610
22
Iida, F., Saitou, K., Kawamura, T., Yamaguchi, S., &
23
Nishimura, T. (2015). Effect of fat content on sensory
24
characteristics of marbled beef from Japanese Black
25
steers. Animal Science Journal, 86, 707-715.
26
https://doi.org/10.1111/asj.12342
20
Inoue, K., Shoji, N., Honda, T., & Oyama, K. (2017). Genetic
relationships between meat quality traits and fatty acid
composition in Japanese black cattle. Animal Science
Journal, 88, 11-18. https://doi.org/10.1111/asj.12613
Irie, M., Oka, A., & Iwaki, F. (2003). Fibre-optic method for
estimation of bovine fat quality. Journal of the Science
of Food and Agriculture, 83, 483-486.
https://doi.org/10.1002/jsfa.1400
10
11
JMGA (Japan Meat Grading Association). (1988). New Beef
Carcass Grading Standards. JMGA, Tokyo, Japan.
Johnson,
D.
L.,
Thompson,
R.
(1995).
Restricted
maximum
12
likelihood estimation of variance components for univariate
13
animal models
using sparse
14
information.
Journal
15
https://doi.org/10.3168/jds.S0022-0302(95)76654-1
of
matrix
Dairy
techniques
Science,
and
78,
average
449-456.
16
Kato, K., Maeda, S., & Kuchida, K. (2014). Genetic parameters
17
for fineness of marbling in M. longissimus thoracis of
18
Japanese Black cattle. Nihon Chikusan Gakkaiho, 85, 21-26.
19
(In Japanese with English summary)
20
https://doi.org/10.2508/chikusan.85.21
21
Kobe Beef Marketing & Distribution Promotion Association.
22
2021. All about Kobe Beef [homepage on the Internet]. Kobe
23
Beef Marketing & Distribution Promotion Association,
24
Hyogo, Japan; [accessed 29 September 2021]. Available from
25
URL:
26
http://www.kobeniku.jp/en/contents/about/definition.html
21
Komatsu, T., Shoji, N., Endo, H., & Suzuki, K. (2021).
Comparison between the gas chromatography and the near-
infrared spectrometry on the estimation of genetic
parameter of the fatty acid composition of intermuscular
fat from Japanese black cattle. Nihon Chikusan Gakkaiho,
92, 41-45. (In Japanese with English summary)
https://doi.org/10.2508/chikusan.92.41
Kuchida, K., Osawa, T., Hori, T., Kotaka, H., & Maruyama, S.
(2006). Evaluation and genetics of carcass cross section
10
of beef carcass by computer image analysis. Journal of
11
Animal Genetics, 34, 45–52. (In Japanese)
12
https://doi.org/10.5924/abgri2000.34.2_45
13
Misztal, I., Tsuruta, S., Lourenco, D. A. L., Masuda, Y.,
14
Aguilar, I., Legarra, A., & Vitezica, Z. (2018). Manual
15
for BLUPF90 family programs. University of Georgia.
16
[accessed 29 September 2021]. Available from URL:
17
http://nce.ads.uga.edu/wiki/doku.php?id=documentation
18
Nakahashi, Y., Maruyama, S., Seki, S., Hidaka, S., & Kuchida,
19
K. (2008). Relationships between monounsaturated fatty
20
acids of marbling flecks and image analysis traits in
21
longissimus muscle for Japanese Black steers. Journal of
22
Animal Science, 86, 3551-3556.
23
https://doi.org/10.2527/jas.2008-0947
24
Nakajima, A., Kawaguchi, F., Uemoto, Y., Fukushima, M.,
25
Yoshida, E., Iwamoto, E., ... Sasazaki, S. (2018). A
26
genome‐wide association study for fat‐related traits
22
computed by image analysis in Japanese Black cattle.
Animal Science Journal, 89, 743-751.
https://doi.org/10.1111/asj.12987
Nishioka, T., Ishizuka, Y., Yasumatsuya, K., & Irie, M.
(2008). Relationships between the meat wholesale market's
and retail stores' preferences and physiochemical traits
of bovine fat. Nihon Chikusan Gakkaiho, 79, 391-401. (In
Japanese with English summary)
https://doi.org/10.2508/chikusan.79.391
10
Nogi, T., Honda, T., Mukai, F., Okagaki, T., & Oyama, K.
11
(2011). Heritabilities and genetic correlations of fatty
12
acid compositions in longissimus muscle lipid with carcass
13
traits in Japanese Black cattle. Journal of Animal
14
Science, 89, 615-621. https://doi.org/10.2527/jas.2009-
15
2300
16
Oka, A., Iwaki, F., Dohgo, T., Ohtagaki, S., Noda, M.,
17
Shiozaki, T., ... Ozaki, M. (2002). Genetic effects on
18
fatty acid composition of carcass fat of Japanese Black
19
Wagyu steers. Journal of Animal Science, 80, 1005-1011.
20
https://doi.org/10.2527/2002.8041005x
21
Ookura, K., Akiyama, T., Yoshida, E., Fukushima, M., Iwamoto,
22
E., Oka, A., ... Mannen, H. (2013). Effects of genes on
23
economically important traits of Japanese Black cattle in
24
Hyogo population. Nihon Chikusan Gakkaiho, 84, 157-162.
25
(In Japanese with English summary)
26
https://doi.org/10.2508/chikusan.84.157
23
Osawa, T., Kuchida, K., Hidaka, S., & Kato, T. (2008).
Genetic parameters for image analysis traits on M.
longissimus thoracis and M. trapezius of carcass cross
section in Japanese Black steers. Journal of Animal
Science, 86, 40-46. https://doi.org/10.2527/jas.2007-0359
Oyama, K. (2011). Genetic variability of Wagyu cattle
estimated by statistical approaches. Animal Science
Journal, 82, 367-373.
https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.1740-
10
11
0929.2011.00895.x
Patterson,
H.
D.,
Thompson, R.
(1971). Recovery of inter-
12
block information when block sizes are unequal. Biometrika,
13
58, 545-554. https://doi.org/10.1093/biomet/58.3.545
14
Piao, S., Okura, T., & Irie, M. (2018). On-site evaluation of
15
Wagyu beef carcasses based on the monounsaturated, oleic,
16
and saturated fatty acid composition using a handheld
17
fiber-optic near-infrared spectrometer. Meat Science, 137,
18
258-264. https://doi.org/10.1016/j.meatsci.2017.11.032
19
Sakuma, H., Saito, K., Sowa, T., Asano, S., Kohira, K.,
20
Okumura, T., ... Kawamura T. (2012). Effect of crude fat
21
content and fatty acid composition on sensory
22
characteristics of M. longissimus dorsi of Japanese Black
23
steers. Nihon Chikusan Gakkaiho, 83, 291-299. (In Japanese
24
with English summary)
25
https://doi.org/10.2508/chikusan.83.291
26
Sasazaki, S., Kawaguchi, F., Nakajima, A., Yamamoto, R.,
24
Akiyama, T., Kohama, N., ... Mannen, H. (2020). Detection
of candidate polymorphisms around the QTL for fat area
ratio to rib eye area on BTA7 using whole‐genome
resequencing in Japanese Black cattle. Animal Science
Journal, 91, e13335. https://doi.org/10.1111/asj.13335
Shojo, M., Okanishi, T., Anada, K., Oyama, K., & Mukai, F.
(2006). Genetic analysis of calf market weight and carcass
traits in Japanese Black cattle. Journal of Animal
Science, 84, 2617-2622. https://doi.org/10.2527/jas.2005-
10
11
720
Suzuki, K., Yokota, S., Shioura, H., Shimazu, T., & Iida, F.
12
(2013). Effect of meat grade, gender of the animal, and
13
fatty acid content on the eating quality of Japanese black
14
beef meat determined using testing panel. Nihon Chikusan
15
Gakkaiho, 84, 375–382. (In Japanese with English summary)
16
https://doi.org/10.2508/chikusan.84.375
17
Taniguchi, M., Utsugi, T., Oyama, K., Mannen, H., Kobayashi,
18
M., Tanabe, Y., ... Tsuji, S. (2004). Genotype of
19
stearoyl-CoA desaturase is associated with fatty acid
20
composition in Japanese Black cattle. Mammalian Genome,
21
15, 142-148. https://doi.org/10.1007/s00335-003-2286-8
22
Tsuyuki, R., Suzuki, K., & Iida, F. (2016). Effect of fatty
23
acid composition on the sensory characteristics of beef
24
steaks. Journal of Cookery Science of Japan, 49, 19-25.
25
(In Japanese with English summary)
26
https://doi.org/10.11402/cookeryscience.49.19
25
Westerling, D. B., & Hedrick, H. B. (1979). Fatty acid
composition of bovine lipids as influenced by diet, sex
and anatomical location and relationship to sensory
characteristics. Journal of Animal Science, 48, 1343-1348.
https://doi.org/10.2527/jas1979.4861343x
Yamamoto, A., Goto, Y., Asa, R., Hagiya, K., & Kuchida, K.
(2020). Estimating genetic parameters for meat quality
traits, image analysis traits and oleic acid of Japanese
brown cattle in Hokkaido. Nihon Chikusan Gakkaiho, 91, 1-
10
11
7. (In Japanese) https://doi.org/10.2508/chikusan.91.1
26
Figure legends
FIGURE 1 Genetic trends expressed as average breeding values
of breeding cows by birth year in genetic standard deviation
units
for
beef
marbling
standard
number
(BMS
No.),
image
analysis traits (FAR, fat area ratio; FI, fineness index) and
monounsaturated
fatty
acid
(MUFA)
measured
by
spectroscopy (NIRS) and gas chromatography (GC).
near-infrared
27
Fig. 1
TABLE 1 Summary statistics for carcass grading traits, image analysis traits, and
monounsaturated fatty acids
Trait
Mean
SD
Minimum
Maximum
Carcass trait
CW (kg)
29,942
404.6
41.8
258.0
562.0
LMA (cm )
29,942
55.0
7.7
32.0
92.0
RT (cm)
29,942
6.9
0.7
4.5
10.5
SFT (cm)
29,942
2.5
0.8
0.5
6.6
BMS No.
29,942
6.8
2.0
2.0
12.0
24,704
47.1
7.3
19.4
74.8
24,704
2.6
0.5
0.5
5.4
18,570
63.2
3.1
45.8
71.8
2,952
59.2
3.6
45.8
71.7
Image analysis trait
FAR (%)
FI (count/cm )
Monounsaturated fatty acid
NIRS MUFA (%)
GC MUFA (%)
Abbreviations: CW, carcass weight; LMA, longissimus muscle area; RT, rib thickness; SFT,
subcutaneous fat thickness; BMS No., beef marbling standard number; FAR, fat area ratio;
FI, fineness index; NIRS MUFA, monounsaturated fatty acid by near-infrared
spectroscopy; GC MUFA, monounsaturated fatty acid by gas chromatography.
TABLE 2 Estimated variances and heritability for carcass grading traits, image analysis traits,
and monounsaturated fatty acids
Trait
Farm
Genetic
Residual
variance
variance
variance
Heritability ± SE
Carcass trait
CW
166.140
685.640
599.630
0.472 ± 0.024
LMA
3.570
36.080
21.241
0.593 ± 0.024
RT
0.047
0.233
0.259
0.432 ± 0.025
SFT
0.047
0.326
0.210
0.559 ± 0.024
BMS No.
0.270
2.809
0.988
0.691 ± 0.023
FAR
3.127
37.106
9.918
0.740 ± 0.024
FI
0.008
0.099
0.143
0.395 ± 0.029
NIRS MUFA
0.529
4.090
4.466
0.450 ± 0.033
GC MUFA
0.808
5.802
4.085
0.542 ± 0.071
Image analysis trait
Monounsaturated fatty acid
See TABLE 1 for abbreviations.
TABLE 3 Estimates of genetic (above diagonal) and phenotypic (below diagonal) correlations among carcass grading traits, image analysis
traits, and monounsaturated fatty acids
Trait
CW
CW
LMA
0.278
(0.035)
RT
SFT
0.535
(0.030)
0.204
(0.039)
BMS No.
FAR
FI
0.130
(0.036)
0.106
(0.036)
-0.197
(0.049)
0.070
(0.051)
0.129
(0.078)
0.229
(0.040)
0.291
(0.035)
0.274
(0.036)
-0.178
(0.052)
0.091
(0.054)
0.069
(0.083)
0.459
RT
0.655
0.384
SFT
0.309
-0.096
0.275
BMS No.
0.175
0.463
0.286
-0.041
FAR
0.133
0.323
0.232
-0.029
0.814
FI
-0.196
-0.159
-0.160
-0.056
-0.085
-0.169
NIRS MUFA
0.077
-0.063
0.084
0.133
-0.072
-0.113
-0.090
GC MUFA
0.124
0.034
0.098
0.085
-0.036
-0.061
-0.145
See TABLE 1 for abbreviations.
GC MUFA
0.222
(0.038)
LMA
Standard errors of genetic correlations are shown in parentheses.
NIRS MUFA
-0.289
(0.036)
0.507
(0.026)
-0.090
(0.034)
0.369
(0.029)
-0.060
(0.034)
0.959
(0.005)
-0.064
(0.047)
-0.089
(0.048)
-0.065
(0.044)
-0.219
(0.042)
-0.203
(0.047)
0.300
(0.047)
-0.228
(0.044)
-0.259
(0.044)
-0.158
(0.060)
0.467
-0.039
(0.076)
0.178
(0.078)
-0.077
(0.074)
-0.168
(0.073)
-0.062
(0.091)
0.804
(0.062)
...