↓ Skip to main content

Genome-wide association study of reproductive traits in Nellore heifers using Bayesian inference

Overview of attention for article published in Genetics Selection Evolution, August 2015
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
42 Dimensions

Readers on

mendeley
91 Mendeley
Title
Genome-wide association study of reproductive traits in Nellore heifers using Bayesian inference
Published in
Genetics Selection Evolution, August 2015
DOI 10.1186/s12711-015-0146-0
Pubmed ID
Authors

Raphael B. Costa, Gregório MF Camargo, Iara DPS Diaz, Natalia Irano, Marina M. Dias, Roberto Carvalheiro, Arione A. Boligon, Fernando Baldi, Henrique N. Oliveira, Humberto Tonhati, Lucia G. Albuquerque

Abstract

An important goal of Zebu breeding programs is to improve reproductive performance. A major problem faced with the genetic improvement of reproductive traits is that recording the time for an animal to reach sexual maturity is costly. Another issue is that accurate estimates of breeding values are obtained only a long time after the young bulls have gone through selection. An alternative to overcome these problems is to use traits that are indicators of the reproductive efficiency of the herd and are easier to measure, such as age at first calving. Another problem is that heifers that have conceived once may fail to conceive in the next breeding season, which increases production costs. Thus, increasing heifer's rebreeding rates should improve the economic efficiency of the herd. Response to selection for these traits tends to be slow, since they have a low heritability and phenotypic information is provided only later in the life of the animal. Genome-wide association studies (GWAS) are useful to investigate the genetic mechanisms that underlie these traits by identifying the genes and metabolic pathways involved. Data from 1853 females belonging to the Agricultural Jacarezinho LTDA were used. Genotyping was performed using the BovineHD BeadChip (777 962 single nucleotide polymorphisms (SNPs)) according to the protocol of Illumina - Infinium Assay II ® Multi-Sample HiScan with the unit SQ ™ System. After quality control, 305 348 SNPs were used for GWAS. Forty-two and 19 SNPs had a Bayes factor greater than 150 for heifer rebreeding and age at first calving, respectively. All significant SNPs for age at first calving were significant for heifer rebreeding. These 42 SNPs were next or within 35 genes that were distributed over 18 chromosomes and comprised 27 protein-encoding genes, six pseudogenes and two miscellaneous noncoding RNAs. The use of Bayes factor to determine the significance of SNPs allowed us to identify two sets of 42 and 19 significant SNPs for heifer rebreeding and age at first calving, respectively, which explain 11.35 % and 6.42 % of their phenotypic variance, respectively. These SNPs provide relevant information to help elucidate which genes affect these traits.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 1%
United States 1 1%
Unknown 89 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 21 23%
Researcher 13 14%
Student > Ph. D. Student 10 11%
Student > Bachelor 8 9%
Student > Doctoral Student 7 8%
Other 16 18%
Unknown 16 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 42 46%
Biochemistry, Genetics and Molecular Biology 9 10%
Veterinary Science and Veterinary Medicine 6 7%
Computer Science 3 3%
Medicine and Dentistry 3 3%
Other 7 8%
Unknown 21 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 August 2015.
All research outputs
#20,656,820
of 25,374,647 outputs
Outputs from Genetics Selection Evolution
#667
of 822 outputs
Outputs of similar age
#203,701
of 277,609 outputs
Outputs of similar age from Genetics Selection Evolution
#10
of 15 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 822 research outputs from this source. They receive a mean Attention Score of 4.1. This one is in the 6th percentile – i.e., 6% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 277,609 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.