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The effect of mislabeled phenotypic status on the identification of mutation-carriers from SNP genotypes in dairy cattle

Overview of attention for article published in BMC Research Notes, June 2017
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Title
The effect of mislabeled phenotypic status on the identification of mutation-carriers from SNP genotypes in dairy cattle
Published in
BMC Research Notes, June 2017
DOI 10.1186/s13104-017-2540-x
Pubmed ID
Authors

Stefano Biffani, Hubert Pausch, Hermann Schwarzenbacher, Filippo Biscarini

Abstract

Statistical and machine learning applications are increasingly popular in animal breeding and genetics, especially to compute genomic predictions for phenotypes of interest. Noise (errors) in the data may have a negative impact on the accuracy of predictions. The effects of noisy data have been investigated in genome-wide association studies for case-control experiments, and in genomic predictions for binary traits in plants. No studies have been published yet on the impact of noisy data in animal genomics. In this work, the susceptibility to noise of five classification models (Lasso-penalised logistic regression-Lasso, K-nearest neighbours-KNN, random forest-RF, support vector machines with linear-SVML-or radial-SVMR-kernel) was tested. As illustration, the identification of carriers of a recessive mutation in cattle (Bos taurus) was used. A population of 3116 Fleckvieh animals with SNP genotypes on the same chromosome as the mutation locus (BTA 19) was available. The carrier status (0/1 phenotype) was randomly sampled to generate noise. Increasing proportions of noise-up to 20%- were introduced in the data. SVMR and Lasso were relatively more robust to noise in the data, with total accuracy still above 0.975 and TPR (true positive rate; accuracy in the minority class) in the range 0.5-0.80 also with 17.5-20% mislabeled observations. The performance of SVML and RF decreased monotonically with increasing noise in the data, while KNN constantly failed to identify mutation carriers (observations in the minority class). The computation time increased with noise in the data, especially for the two support vector machines classifiers. This work was the first to assess the impact of phenotyping errors on the accuracy of genomic predictions in animal genetics. The choice of the classification method can influence results in terms of higher or lower susceptibility to noise. In the presented problem, SVM with radial kernel performed relatively well even when the proportion of errors in the data reached 12.5%. Lasso was the second best method, while SVML, RF and KNN were very sensitive to noise. Taking into account both accuracy and computation time, Lasso provided the best combination.

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 18%
Student > Master 4 18%
Student > Ph. D. Student 4 18%
Professor 1 5%
Student > Doctoral Student 1 5%
Other 2 9%
Unknown 6 27%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 23%
Biochemistry, Genetics and Molecular Biology 3 14%
Computer Science 3 14%
Veterinary Science and Veterinary Medicine 1 5%
Psychology 1 5%
Other 1 5%
Unknown 8 36%

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 30 January 2018.
All research outputs
#7,804,393
of 12,439,436 outputs
Outputs from BMC Research Notes
#1,410
of 2,780 outputs
Outputs of similar age
#193,427
of 341,197 outputs
Outputs of similar age from BMC Research Notes
#21
of 42 outputs
Altmetric has tracked 12,439,436 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,780 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
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We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.