| Title |
Comparison of significant single nucleotide polymorphisms selections in GWAS for complex traits
|
|---|---|
| Published in |
Journal of Applied Genetics, August 2015
|
| DOI | 10.1007/s13353-015-0305-6 |
| Pubmed ID | |
| Authors |
M. Frąszczak, J. Szyda |
| Abstract |
The goal of this study was to compare significant SNP selection approaches in the context of complex traits based on SNP estimates obtained by models: a model fitting a single SNP (M1), a model fitting a single SNP and a random polygenic effect (M2), the nonparametric CAR score (M3), a SNP-BLUP model with random effects of all SNPs fitted simultaneously (M4). There were 46,267 SNPs tested in a population of 2601 Holstein Friesian bulls, four traits (milk and fat yields, somatic cell score, non-return rate for heifers) were considered. The numbers of SNPs selected as significant differed among models. M1 selected a very large number of SNPs, except for a NRH in which no SNPs were significant. M2 and M3 both selected similar and low number of SNPs for each trait. M4 selected more SNPs than M2 and M3. Considering linkage disequilibrium between SNPs, for MY M2 and M3 selected SNPs more highly correlated with each other than in the case of M4, while for FY M3 selection contained more correlated SNPs than M2 and M4. In conclusion, if the research interest is to identify SNPs not only with strong, but also with moderate effects on a complex trait a multiple-SNP model is recommended. Such models are capable of accounting for at least a part of linkage disequilibrium between SNPs through the design matrix of SNP effects. Functional annotation of SNPs significant in M4 reveals good correspondence between selected polymorphisms and functional information as well as with QTL mapping results. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Poland | 1 | 4% |
| Unknown | 24 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 28% |
| Researcher | 5 | 20% |
| Student > Master | 3 | 12% |
| Other | 2 | 8% |
| Professor | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 6 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 56% |
| Veterinary Science and Veterinary Medicine | 2 | 8% |
| Biochemistry, Genetics and Molecular Biology | 1 | 4% |
| Computer Science | 1 | 4% |
| Medicine and Dentistry | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 6 | 24% |
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 22 August 2015.
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#15,344,095
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#181
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#156,316
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Outputs of similar age from Journal of Applied Genetics
#7
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