The increasing availability of SNP (single nucleotide polymorphisms) genotype data in livestock is stimulating the development of new data analysis strategies, which can be applied in animal breeding. One possible application is the prediction of carriers of specific haplotypes, especially if they impact animal health. It is therefore convenient to have a practical and easy-to-implement statistical method for the accurate classification of individuals into carriers and non-carriers. In this paper, we present a procedure for the identification of carriers of the haplotype HH1 on BTA5 (Bos Taurus autosome 5), which is known to be associated with reduced cow fertility in Holstein-Friesian cattle. A population of 1104 Holstein bulls genotyped with the 54K SNP-chip was available for the analysis. There were 45 carriers (5.3%) and 1045 non-carriers (94.7%). Two complementary multivariate statistical techniques were used for the identification of haplotype carriers: Backward Stepwise Selection (BSS) to select the SNP that best fit the model, and Linear Discriminant Analysis (LDA) to classify observations, based on the selected SNP, into carriers and non-carriers. In order to explore the minimum-sized set of SNP that correctly identifies haplotype carriers, different proportions of SNP were tested: 2.5; 10; 15; 30; 50 and 100%. For each proportion of SNP, BSS and LDA were applied, and the classification error rate was estimated in a 10-fold cross-validation scheme. Data were split in 10 subsets. The first subset was treated as validation set, while the model was fit on the remaining nine subsets (the training set). The overall error rate for the prediction of haplotype carriers was on average very low (∼1%) both in the training and in the validation datasets. The error rate was found to depend on the number of SNPs in the model and their density around the region of the haplotype on BTA5. The minimum set of SNPs to achieve accurate predictions was 18, with a total test error rate of 1.27. This paper describes a procedure to accurately identify haplotype carriers from SNP genotypes in cattle populations. Very few misclassifications were observed, which indicates that this is a very reliable approach for potential applications in cattle breeding.

1. A statistical learning approach to
detect carriers of the HH1 haplotype
in Italian Holstein Friesian cattle
Stefano Biffani*, Stefania Chessa,
Alessandra Stella and Filippo Biscarini1
*IBBA-CNR (UOS-Lodi),
1 Department of Bioinformatics, (PTP-LODI)

2. Background
The advent of Genotyping
GWAS
GEBV
PARENTAGE/
POP STRUCTURE
CARRIERS/
NON CARRIERS

3. Background
The advent of Genotyping
Statistical Learning:
Tools for understanding data

4. Background
• Haplotype or gene allele prediction from
marker genotypes
• K-casein
• Embryo – losses
• Perinatal mortality

5. Background
• Haplotype reconstruction & Imputation
Extended pedigree
Information
Time consuming
Computationally
demanding
• BEAGLE
• IMPUTE2
• MACH
• FINDHAP
• PEDIMPUTE
• FIMPUTE
• …. Nicolazzi et al –
2015 Animal Genetics.
doi: 10.1111/age.12295

6. The idea
CLASS LABEL FEATURES (INPUT DATA)
CARRIER/NOT
CARRIER
SNP1 SNP2 …. SNP100 … SNP1000 …. SNPn
YES 0 1 … 1 2 1
YES 2 2 0 2 0
NO 2 1 1 1 1
YES 1 2 1 2 1
YES 1 0 2 1 2
NO 1 0 2 2 1
NO 1 0 2 0 0

7. The idea

8. The idea
• LET’S APPLY A SIMPLE LINEAR CLASSIFICATION
METHOD FOR:
– the prediction of specific haplotype carriers from
SNP data
– HH1 on BTA5 - reduced cow fertility
– Original idea applied to Italian Brown Swiss
(Genetics Selection Evolution 2015, 47:4)

9. Data
• 1104 Holstein bulls
• genotyped with the 54K SNP-chip
• Only SNPs on BTA5 (2225)
• Carriers = 5.3% - non-carriers = 94.7%

10. Methods
Using 2.5; 10; 15; 30; 50 and 100% of SNPs on BTA5
– For each threshold:
1. Check linear dependencies and collinearity (r = 0.7)
2. Backward Stepwise Selection (BSS) to select the SNP
that best fit the model,
3. Linear Discriminant Analysis (LDA) to classify
observations

11. Methods
• For each threshold a 10-fold cross-validation
scheme was used (repeated 100 times)

12. Statistics
1.Total Error Rate (TER)
2.False Positive Rate (FPR) # non-carriers
misclassified as carriers over the total #
non carriers;
3.False Negative Rate (FNR) # carriers
misclassified as non-carriers over the total
# carriers.

13. Results- Total Error Rate

14. Results- False Positive Rate (FPR)

15. Results- False Negative Rate (FNR)

16. Additional considerations
• For each threshold we have 1000 replicates
– Count the # of times a SNP was selected
– genomic regions that harbour SNPs most relevant
for prediction can be identified
BTA 5 – SNPs position (Mbp)
60 70
HH1 Region

17. Conclusions
• Simple, fast and reliable method to accurately
identify haplotype carriers from SNP
genotypes
• The classification step can in principle be
replaced by any other linear or nonlinear
classification method (e.g. LR, SVM)

18. Conclusions
• The methodology can be applied to different
situations (e.g., causal mutation)
• Cross-validation: the right way – assign
observation to the training and testing sets
before the variable selection and classification
steps

19. THANKS