| Abstract |
The aims of this study were (1) to identify genomic
regions associated with a N efficiency index (NEI) and
its composition traits and (2) to analyze the functional
annotation of identified genomic regions. The
NEI included N intake (NINT1), milk true protein N
(MTPN1), milk urea N yield (MUNY1) in primiparous
cattle, and N intake (NINT2+), milk true protein N
(MTPN2+), and milk urea N yield (MUNY2+) in
multiparous cattle (2 to 5 parities). The edited data included
1,043,171 records on 342,847 cows distributed in
1,931 herds. The pedigree consisted of 505,125 animals
(17,797 males). Data of 565,049 SNPs were available for
6,998 animals included in the pedigree (5,251 females
and 1,747 males). The SNP effects were estimated using
a single-step genomic BLUP approach. The proportion
of the total additive genetic variance explained by windows
of 50 consecutive SNPs (with an average size of
about 240 kb) was calculated. The top 3 genomic regions
explaining the largest rate of the total additive genetic
variance of the NEI and its composition traits were
selected for candidate gene identification and quantitative
trait loci (QTL) annotation. The selected genomic
regions explained from 0.17% (MTPN2+) to 0.58%
(NEI) of the total additive genetic variance. The largest
explanatory genomic regions of NEI, NINT1, NINT2+,
MTPN1, MTPN2+, MUNY1, and MUNY2+ were Bos
taurus autosome 14 (1.52–2.09 Mb), 26 (9.24–9.66 Mb),
16 (75.41–75.51 Mb), 6 (8.73–88.92 Mb), 6 (8.73–88.92
Mb), 11 (103.26–103.41 Mb), 11 (103.26–103.41 Mb).
Based on the literature, gene ontology, Kyoto Encyclopedia
of Genes and Genomes, and protein-protein
interaction, 16 key candidate genes were identified for
NEI and its composition traits, which are mainly expressed
in the milk cell, mammary, and liver tissues.
The number of enriched QTL related to NEI, NINT1,
NINT2+, MTPN1, and MTPN2+ were 41, 6, 4, 11, 36,
32, and 32, respectively, and most of them were related
to the milk, health, and production classes. In conclusion,
this study identified genomic regions associated
with NEI and its composition traits, and identified key
candidate genes describing the genetic mechanisms of
N use efficiency-related traits. Furthermore, the NEI
reflects not only its composition traits but also the interactions
among them. |
| Authors |
Chen, Y., Atashi, H., Grelet, C., R. Mota, R., Vanderick, S., Hu, H., GplusE Consortium, Gengler, N. |
