You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Whole genome population genetics analysis of Sudanese goats identifies regions harboring genes associated with major traits
|
|---|---|
| Published in |
BMC Genomic Data, October 2017
|
| DOI | 10.1186/s12863-017-0553-z |
| Pubmed ID | |
| Authors |
Siham A. Rahmatalla, Danny Arends, Monika Reissmann, Ammar Said Ahmed, Klaus Wimmers, Henry Reyer, Gudrun A. Brockmann |
| Abstract |
Sudan is endowed with a variety of indigenous goat breeds which are used for meat and milk production and which are well adapted to the local environment. The aim of the present study was to determine the genetic diversity and relationship within and between the four main Sudanese breeds of Nubian, Desert, Taggar and Nilotic goats. Using the 50 K SNP chip, 24 animals of each breed were genotyped. More than 96% of high quality SNPs were polymorphic with an average minor allele frequency of 0.3. In all breeds, no significant difference between observed (0.4) and expected (0.4) heterozygosity was found and the inbreeding coefficients (FIS) did not differ from zero. Fst coefficients for the genetic distance between breeds also did not significantly deviate from zero. In addition, the analysis of molecular variance revealed that 93% of the total variance in the examined population can be explained by differences among individuals, while only 7% result from differences between the breeds. These findings provide evidence for high genetic diversity and little inbreeding within breeds on one hand, and low diversity between breeds on the other hand. Further examinations using Nei's genetic distance and STRUCTURE analysis clustered Taggar goats distinct from the other breeds. In a principal component (PC) analysis, PC1 could separate Taggar, Nilotic and a mix of Nubian and Desert goats into three groups. The SNPs that contributed strongly to PC1 showed high Fst values in Taggar goat versus the other goat breeds. PCA allowed us to identify target genomic regions which contain genes known to influence growth, development, bone formation and the immune system. The information on the genetic variability and diversity in this study confirmed that Taggar goat is genetically different from the other goat breeds in Sudan. The SNPs identified by the first principal components show high Fst values in Taggar goat and allowed to identify candidate genes which can be used in the development of breed selection programs to improve local breeds and find genetic factors contributing to the adaptation to harsh environments. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 47 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 17% |
| Researcher | 5 | 11% |
| Student > Master | 5 | 11% |
| Student > Doctoral Student | 4 | 9% |
| Professor | 3 | 6% |
| Other | 8 | 17% |
| Unknown | 14 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 20 | 43% |
| Biochemistry, Genetics and Molecular Biology | 6 | 13% |
| Veterinary Science and Veterinary Medicine | 3 | 6% |
| Computer Science | 1 | 2% |
| Medicine and Dentistry | 1 | 2% |
| Other | 0 | 0% |
| Unknown | 16 | 34% |
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 12 December 2017.
All research outputs
#19,951,180
of 25,382,440 outputs
Outputs from BMC Genomic Data
#786
of 1,204 outputs
Outputs of similar age
#246,956
of 338,323 outputs
Outputs of similar age from BMC Genomic Data
#6
of 14 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 28th percentile – i.e., 28% 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 338,323 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.