Staff Members - Benha University
Genetic Analysis of Milk Production Traits and Mid-Infrared Spectra in Chinese Holstein Population
animals • 2020
Publication Information
Authors
Chao Du 1
, Liangkang Nan 1
, Lei Yan 2
, Qiuyue Bu 2
, Xiaoli Ren 2
, Zhen Zhang 2
,
Ahmed Sabek 1,3 and Shujun Zhang 1,*
Keywords
mid-infrared spectra; milk production traits; spectral wavenumbers; heritability;
genetic correlation
Journal
animals
Publisher
MDPI
Volume
10
Issue
1
Pages
Not Available
publication.type
International
Paper Link
Open Link
Supplementary Materials
Not Available
Abstract
Usually, spectral data are used as predictors to predict milk components, animal
characteristics, and even reproductive status. Another innovative way to use spectral data involves
considering spectral wavenumbers as traits and then analyzing from the genetic perspective. In this
study, we considered milk spectral data directly as traits, then detected the influence of some
non-genetic factors on spectral wavenumbers and estimated the genetic parameters of spectral points.
The result of the present study could be used as a management tool for dairy farm and also provides
a further understanding of genetic background of milk mid-infrared (MIR) spectra. In future, milk
spectral data could be applied more effective. For example, some sub-clinical diseases might be
detected based on the difference between the expected and observed values of the spectral traits.
In addition, we could also use genetic correlation between wavenumbers and a trait of interest, which
are difficult and expensive to measure, to apply for the genetic improvement of dairy species.
characteristics, and even reproductive status. Another innovative way to use spectral data involves
considering spectral wavenumbers as traits and then analyzing from the genetic perspective. In this
study, we considered milk spectral data directly as traits, then detected the influence of some
non-genetic factors on spectral wavenumbers and estimated the genetic parameters of spectral points.
The result of the present study could be used as a management tool for dairy farm and also provides
a further understanding of genetic background of milk mid-infrared (MIR) spectra. In future, milk
spectral data could be applied more effective. For example, some sub-clinical diseases might be
detected based on the difference between the expected and observed values of the spectral traits.
In addition, we could also use genetic correlation between wavenumbers and a trait of interest, which
are difficult and expensive to measure, to apply for the genetic improvement of dairy species.