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Mendeley readers
Attention Score in Context
| Title |
A model to estimate effects of SNPs on host susceptibility and infectivity for an endemic infectious disease
|
|---|---|
| Published in |
Genetics Selection Evolution, June 2017
|
| DOI | 10.1186/s12711-017-0327-0 |
| Pubmed ID | |
| Authors |
Floor Biemans, Mart C. M. de Jong, Piter Bijma |
| Abstract |
Infectious diseases in farm animals affect animal health, decrease animal welfare and can affect human health. Selection and breeding of host individuals with desirable traits regarding infectious diseases can help to fight disease transmission, which is affected by two types of (genetic) traits: host susceptibility and host infectivity. Quantitative genetic studies on infectious diseases generally connect an individual's disease status to its own genotype, and therefore capture genetic effects on susceptibility only. However, they usually ignore variation in exposure to infectious herd mates, which may limit the accuracy of estimates of genetic effects on susceptibility. Moreover, genetic effects on infectivity will exist as well. Thus, to design optimal breeding strategies, it is essential that genetic effects on infectivity are quantified. Given the potential importance of genetic effects on infectivity, we set out to develop a model to estimate the effect of single nucleotide polymorphisms (SNPs) on both host susceptibility and host infectivity. To evaluate the quality of the resulting SNP effect estimates, we simulated an endemic disease in 10 groups of 100 individuals, and recorded time-series data on individual disease status. We quantified bias and precision of the estimates for different sizes of SNP effects, and identified the optimum recording interval when the number of records is limited. We present a generalized linear mixed model to estimate the effect of SNPs on both host susceptibility and host infectivity. SNP effects were on average slightly underestimated, i.e. estimates were conservative. Estimates were less precise for infectivity than for susceptibility. Given our sample size, the power to estimate SNP effects for susceptibility was 100% for differences between genotypes of a factor 1.56 or more, and was higher than 60% for infectivity for differences between genotypes of a factor 4 or more. When disease status was recorded 11 times on each animal, the optimal recording interval was 25 to 50% of the average infectious period. Our model was able to estimate genetic effects on susceptibility and infectivity. In future genome-wide association studies, it may serve as a starting point to identify genes that affect disease transmission and disease prevalence. |
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 % |
|---|---|---|
| Netherlands | 1 | 33% |
| United Kingdom | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Professor | 4 | 20% |
| Researcher | 3 | 15% |
| Student > Master | 3 | 15% |
| Student > Bachelor | 2 | 10% |
| Other | 1 | 5% |
| Other | 3 | 15% |
| Unknown | 4 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Veterinary Science and Veterinary Medicine | 5 | 25% |
| Agricultural and Biological Sciences | 5 | 25% |
| Biochemistry, Genetics and Molecular Biology | 2 | 10% |
| Unspecified | 1 | 5% |
| Immunology and Microbiology | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 5 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 04 July 2017.
All research outputs
#15,173,117
of 25,382,440 outputs
Outputs from Genetics Selection Evolution
#441
of 821 outputs
Outputs of similar age
#171,219
of 327,487 outputs
Outputs of similar age from Genetics Selection Evolution
#8
of 13 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 821 research outputs from this source. They receive a mean Attention Score of 4.1. This one is in the 44th percentile – i.e., 44% 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 327,487 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.