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Genome-wide association analysis and genomic prediction of Mycobacterium avium subspecies paratuberculosis infection in US Jersey cattle.

Zare Y, Shook GE, Collins MT, Kirkpatrick BW - PLoS ONE (2014)

Bottom Line: Correspondence between results of GRAMMAR-GC and Bayes C was high (70-80% of most significant SNPs in common).These SNPs could potentially be associated with causal variants underlying susceptibility to MAP infection in Jersey cattle.Predictive performance of the model developed by Bayes C for prediction of infection status of animals in validation set was low (55% probability of correct ranking of paired case and control samples).

View Article: PubMed Central - PubMed

Affiliation: College of Agricultural and Life Sciences, Department of Animal Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.

ABSTRACT
Paratuberculosis (Johne's disease), an enteric disorder in ruminants caused by Mycobacterium avium subspecies paratuberculosis (MAP), causes economic losses in excess of $200 million annually to the US dairy industry. To identify genomic regions underlying susceptibility to MAP infection in Jersey cattle, a case-control genome-wide association study (GWAS) was performed. Blood and fecal samples were collected from ∼ 5,000 mature cows in 30 commercial Jersey herds from across the US. Discovery data consisted of 450 cases and 439 controls genotyped with the Illumina BovineSNP50 BeadChip. Cases were animals with positive ELISA and fecal culture (FC) results. Controls were animals negative to both ELISA and FC tests that matched cases on birth date and herd. Validation data consisted of 180 animals including 90 cases (positive to FC) and 90 controls (negative to ELISA and FC), selected from discovery herds and genotyped by Illumina BovineLD BeadChip (∼ 7K SNPs). Two analytical approaches were used: single-marker GWAS using the GRAMMAR-GC method and Bayesian variable selection (Bayes C) using GenSel software. GRAMMAR-GC identified one SNP on BTA7 at 68 megabases (Mb) surpassing a significance threshold of 5 × 10(-5). ARS-BFGL-NGS-11887 on BTA23 (27.7 Mb) accounted for the highest percentage of genetic variance (3.3%) in the Bayes C analysis. SNPs identified in common by GRAMMAR-GC and Bayes C in both discovery and combined data were mapped to BTA23 (27, 29 and 44 Mb), 3 (100, 101, 106 and 107 Mb) and 17 (57 Mb). Correspondence between results of GRAMMAR-GC and Bayes C was high (70-80% of most significant SNPs in common). These SNPs could potentially be associated with causal variants underlying susceptibility to MAP infection in Jersey cattle. Predictive performance of the model developed by Bayes C for prediction of infection status of animals in validation set was low (55% probability of correct ranking of paired case and control samples).

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Receiver Operating Characteristic (ROC) curve for validation data.Multi-SNP model was developed by Bayes C analysis of discovery data and tested in classifying 180 case vs. control animals in validation data. Broken line represents the model. Area under ROC curve is equivalent to the probability of correctly assigning a random pair of observations (positive and negative) to case and control. The diagonal represents a model with no predictive ability (AUC = 0.5).
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pone-0088380-g003: Receiver Operating Characteristic (ROC) curve for validation data.Multi-SNP model was developed by Bayes C analysis of discovery data and tested in classifying 180 case vs. control animals in validation data. Broken line represents the model. Area under ROC curve is equivalent to the probability of correctly assigning a random pair of observations (positive and negative) to case and control. The diagonal represents a model with no predictive ability (AUC = 0.5).

Mentions: The marker effect estimates from the Bayesian analysis were used to predict the genomic merit of 180 animals in the validation set. The predicted genomic merit was used to rank paired case and control samples which was compared with observed phenotype by ROC analysis. The predictive ability of the model was low. The AUC of the SNP model was 0.55 (Figure 3). The AUC from 10-fold-cross validation using discovery data was similar, ranging between 0.47 to 0.67 (average 0.56) (Figure 4-A).


Genome-wide association analysis and genomic prediction of Mycobacterium avium subspecies paratuberculosis infection in US Jersey cattle.

Zare Y, Shook GE, Collins MT, Kirkpatrick BW - PLoS ONE (2014)

Receiver Operating Characteristic (ROC) curve for validation data.Multi-SNP model was developed by Bayes C analysis of discovery data and tested in classifying 180 case vs. control animals in validation data. Broken line represents the model. Area under ROC curve is equivalent to the probability of correctly assigning a random pair of observations (positive and negative) to case and control. The diagonal represents a model with no predictive ability (AUC = 0.5).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3921184&req=5

pone-0088380-g003: Receiver Operating Characteristic (ROC) curve for validation data.Multi-SNP model was developed by Bayes C analysis of discovery data and tested in classifying 180 case vs. control animals in validation data. Broken line represents the model. Area under ROC curve is equivalent to the probability of correctly assigning a random pair of observations (positive and negative) to case and control. The diagonal represents a model with no predictive ability (AUC = 0.5).
Mentions: The marker effect estimates from the Bayesian analysis were used to predict the genomic merit of 180 animals in the validation set. The predicted genomic merit was used to rank paired case and control samples which was compared with observed phenotype by ROC analysis. The predictive ability of the model was low. The AUC of the SNP model was 0.55 (Figure 3). The AUC from 10-fold-cross validation using discovery data was similar, ranging between 0.47 to 0.67 (average 0.56) (Figure 4-A).

Bottom Line: Correspondence between results of GRAMMAR-GC and Bayes C was high (70-80% of most significant SNPs in common).These SNPs could potentially be associated with causal variants underlying susceptibility to MAP infection in Jersey cattle.Predictive performance of the model developed by Bayes C for prediction of infection status of animals in validation set was low (55% probability of correct ranking of paired case and control samples).

View Article: PubMed Central - PubMed

Affiliation: College of Agricultural and Life Sciences, Department of Animal Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.

ABSTRACT
Paratuberculosis (Johne's disease), an enteric disorder in ruminants caused by Mycobacterium avium subspecies paratuberculosis (MAP), causes economic losses in excess of $200 million annually to the US dairy industry. To identify genomic regions underlying susceptibility to MAP infection in Jersey cattle, a case-control genome-wide association study (GWAS) was performed. Blood and fecal samples were collected from ∼ 5,000 mature cows in 30 commercial Jersey herds from across the US. Discovery data consisted of 450 cases and 439 controls genotyped with the Illumina BovineSNP50 BeadChip. Cases were animals with positive ELISA and fecal culture (FC) results. Controls were animals negative to both ELISA and FC tests that matched cases on birth date and herd. Validation data consisted of 180 animals including 90 cases (positive to FC) and 90 controls (negative to ELISA and FC), selected from discovery herds and genotyped by Illumina BovineLD BeadChip (∼ 7K SNPs). Two analytical approaches were used: single-marker GWAS using the GRAMMAR-GC method and Bayesian variable selection (Bayes C) using GenSel software. GRAMMAR-GC identified one SNP on BTA7 at 68 megabases (Mb) surpassing a significance threshold of 5 × 10(-5). ARS-BFGL-NGS-11887 on BTA23 (27.7 Mb) accounted for the highest percentage of genetic variance (3.3%) in the Bayes C analysis. SNPs identified in common by GRAMMAR-GC and Bayes C in both discovery and combined data were mapped to BTA23 (27, 29 and 44 Mb), 3 (100, 101, 106 and 107 Mb) and 17 (57 Mb). Correspondence between results of GRAMMAR-GC and Bayes C was high (70-80% of most significant SNPs in common). These SNPs could potentially be associated with causal variants underlying susceptibility to MAP infection in Jersey cattle. Predictive performance of the model developed by Bayes C for prediction of infection status of animals in validation set was low (55% probability of correct ranking of paired case and control samples).

Show MeSH
Related in: MedlinePlus