Meta-analysis of two genome-wide association studies of bovine paratuberculosis

Background

Bovine paratuberculosis (ParaTB) also known as Johne''s disease, is a contagious fatal disease resulting from infection by Mycobacterium avium subspecies paratuberculosis (MAP). Previous studies have identified loci associated with ParaTB using different measurements to define cases and controls. The objective of this study was to combine the data from two recent studies to identify genetic loci associated with MAP tissue infection and humoral immune response, defined by MAP ELISA-positive cattle, by comparing cases and control animals for one or both measures of infection.

Methodology/Principal Findings

The two populations used for the association analyses were a cohort of MAP tissue infected animals and control Holstein cows from the USA and the second cohort composed of ELISA-positive and ELISA-negative Holstein cows from Italy. Altogether 1190 cattle were genotyped with the Illumina BovineSNP50 BeadChip. SNP markers were removed if the minor allele frequency <0.01 or genotyping failure was >5%. Animals were removed with >5% genotyping failure. Whole genome association analyses were conducted with the GRAMMAR-CG method using two different definitions of control populations.

Conclusion/Significance

The analyses identified several loci (P<5 e-05) associated with ParaTB, defined by positive ELISA and presence of bacteria in tissue compared to ELISA and tissue negative animals, on chromosomes 1, 12 and 15 and one unassigned SNP. These results confirmed associations on chromosome 12 and the unassigned SNP with ParaTB which had been found in the Italian population alone. Furthermore, several additional genomic regions were found associated with ParaTB when ELISA and tissue positive animals were compared with tissue negative samples. These loci were on chromosomes 1, 6, 7, 13, 16, 21,23 and 25 (P<5 e-05). The results clearly indicate the importance of the phenotype definition when seeking to identify markers associated with different disease responses.     

Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers

Background

At the current price, the use of high-density single nucleotide polymorphisms (SNP) genotyping assays in genomic selection of dairy cattle is limited to applications involving elite sires and dams. The objective of this study was to evaluate the use of low-density assays to predict direct genomic value (DGV) on five milk production traits, an overall conformation trait, a survival index, and two profit index traits (APR, ASI).

Methods

Dense SNP genotypes were available for 42,576 SNP for 2,114 Holstein bulls and 510 cows. A subset of 1,847 bulls born between 1955 and 2004 was used as a training set to fit models with various sets of pre-selected SNP. A group of 297 bulls born between 2001 and 2004 and all cows born between 1992 and 2004 were used to evaluate the accuracy of DGV prediction. Ridge regression (RR) and partial least squares regression (PLSR) were used to derive prediction equations and to rank SNP based on the absolute value of the regression coefficients. Four alternative strategies were applied to select subset of SNP, namely: subsets of the highest ranked SNP for each individual trait, or a single subset of evenly spaced SNP, where SNP were selected based on their rank for ASI, APR or minor allele frequency within intervals of approximately equal length.

Results

RR and PLSR performed very similarly to predict DGV, with PLSR performing better for low-density assays and RR for higher-density SNP sets. When using all SNP, DGV predictions for production traits, which have a higher heritability, were more accurate (0.52-0.64) than for survival (0.19-0.20), which has a low heritability. The gain in accuracy using subsets that included the highest ranked SNP for each trait was marginal (5-6%) over a common set of evenly spaced SNP when at least 3,000 SNP were used. Subsets containing 3,000 SNP provided more than 90% of the accuracy that could be achieved with a high-density assay for cows, and 80% of the high-density assay for young bulls.

Conclusions

Accurate genomic evaluation of the broader bull and cow population can be achieved with a single genotyping assays containing ~ 3,000 to 5,000 evenly spaced SNP.     

Consistency of linkage disequilibrium between Chinese and Nordic Holsteins and genomic prediction for Chinese Holsteins using a joint reference population

Background

In China, the reference population of genotyped Holstein cattle is relatively small with to date, 80 bulls and 2091 cows genotyped with the Illumina 54 K chip. Including genotyped Holstein cattle from other countries in the reference population could improve the accuracy of genomic prediction of the Chinese Holstein population. This study investigated the consistency of linkage disequilibrium between adjacent markers between the Chinese and Nordic Holstein populations, and compared the reliability of genomic predictions based on the Chinese reference population only or the combined Chinese and Nordic reference populations.

Methods

Genomic estimated breeding values of Chinese Holstein cattle were predicted using a single-trait GBLUP model based on the Chinese reference dataset, and using a two-trait GBLUP model based on a joint reference dataset that included both the Chinese and Nordic Holstein data.

Results

The extent of linkage disequilibrium was similar in the Chinese and Nordic Holstein populations and the consistency of linkage disequilibrium between the two populations was very high, with a correlation of 0.97. Genomic prediction using the joint versus the Chinese reference dataset increased reliabilities of genomic predictions of Chinese Holstein bulls in the test data from 0.22, 0.15 and 0.11 to 0.51, 0.47 and 0.36 for milk yield, fat yield and protein yield, respectively. Using five-fold cross-validation, reliabilities of genomic predictions of Chinese cows increased from 0.15, 0.12 and 0.15 to 0.26, 0.17 and 0.20 for milk yield, fat yield and protein yield, respectively.

Conclusions

The linkage disequilibrium between the two populations was very consistent and using the combined Nordic and Chinese reference dataset substantially increased reliabilities of genomic predictions for Chinese Holstein cattle.     

The effect of smoking and alcohol consumption on markers of systemic inflammation,immunoglobulin levels and immune response following pneumococcal vaccination in patients with arthritis

Introduction

The purpose of this research was to study the influence of cigarette smoking and alcohol consumption on immune response to heptavalent pneumococcal conjugate vaccine, immunoglobulin levels (Ig) and markers of systemic inflammation in patients with rheumatoid arthritis (RA) or spondylarthropathy (SpA).

Methods

In total, 505 patients were vaccinated. Six pre-specified groups were enrolled: RA on methotrexate (MTX) treatment in some cases other disease-modifying antirheumatic drugs (DMARDs) (I); RA on anti-tumour necrosis factor (TNF) as monotherapy (II); RA on anti-TNF+MTX+ possibly other DMARDs (III); SpA on anti-TNF as monotherapy (IV); SpA on anti-TNF+MTX+ possibly other DMARDs (V); and SpA on nonsteroidal anti-inflammatory drugs (NSAIDs) and/or analgesics (VI). Smoking (pack-years) and alcohol consumption (g/week) were calculated from patient questionnaires. Ig, C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) were determined at vaccination. IgG antibodies against serotypes 23F and 6B were measured at vaccination and after four to six weeks using standard ELISA. Immune response (ratio between post- and pre-vaccination antibodies; immune response (IR)) and positive immune response (≥2-fold increase in pre-vaccination antibodies; posIR) were calculated.

Results

Eighty-eight patients (17.4%) were current smokers. Smokers had higher CRP and ESR, lower IgG and lower IR for both serotypes (P between 0.012 and 0.045). RA patients on MTX who smoked ≥1pack-year had lower posIR for both serotypes (P = 0.021; OR 0.29; CI 0.1 to 0.7) compared to never-smokers. Alcohol consumption was associated with lower CRP (P = 0.05) and ESR (P = 0.003) but did not influence IR or Ig levels.

Conclusion

Smoking predicted impaired immune response to pneumococcal conjugate vaccine in RA patients on MTX. Smokers with arthritis had higher inflammatory markers and lower IgG regardless of diagnosis and treatment. Low to moderate alcohol consumption was related to lower levels of inflammation markers but had no impact on immune response.

Trial registration

EudraCT EU 2007-006539-29 and NCT00828997     

Susceptibility loci revealed for bovine respiratory disease complex in pre-weaned holstein calves

Background

Bovine respiratory disease complex (BRDC) is an infectious disease of cattle that is caused by a combination of viral and/or bacterial pathogens. Selection for cattle with reduced susceptibility to respiratory disease would provide a permanent tool for reducing the prevalence of BRDC. The objective of this study was to identify BRDC susceptibility loci in pre-weaned Holstein calves as a prerequisite to using genetic improvement as a tool for decreasing the prevalence of BRDC. High density SNP genotyping with the Illumina BovineHD BeadChip was conducted on 1257 male and 757 female Holstein calves from California (CA), and 767 calves identified as female from New Mexico (NM). Of these, 1382 were classified as BRDC cases, and 1396 were classified as controls, with all phenotypes assigned using the McGuirk health scoring system. During the acquisition of blood for DNA isolation, two deep pharyngeal and one mid-nasal diagnostic swab were obtained from each calf for the identification of bacterial and viral pathogens. Genome-wide association analyses were conducted using four analytical approaches (EIGENSTRAT, EMMAX-GRM, GBLUP and FvR). The most strongly associated SNPs from each individual analysis were ranked and evaluated for concordance. The heritability of susceptibility to BRDC in pre-weaned Holstein calves was estimated.

Results

The four statistical approaches produced highly concordant results for 373 top ranked SNPs that defined 126 chromosomal regions for the CA population. Similarly, in NM, 370 SNPs defined 138 genomic regions that were identified by all four approaches. When the two populations were combined (i.e., CA + NM) and analyzed, 324 SNPs defined 116 genomic regions that were associated with BRDC across all analytical methods. Heritability estimates for BRDC were 21% for both CA and NM as individual populations, but declined to 13% when the populations were combined.

Conclusions

Four analytical approaches utilizing both single and multi-marker association methods revealed common genomic regions associated with BRDC susceptibility that can be further characterized and used for genomic selection. Moderate heritability estimates were observed for BRDC susceptibility in pre-weaned Holstein calves, thereby supporting the application of genomic selection to reduce the prevalence of BRDC in U.S. Holsteins.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1164) contains supplementary material, which is available to authorized users.     

Improving the accuracy of genomic prediction in Chinese Holstein cattle by using one-step blending

Background

The one-step blending approach has been suggested for genomic prediction in dairy cattle. The core of this approach is to incorporate pedigree and phenotypic information of non-genotyped animals. The objective of this study was to investigate the improvement of the accuracy of genomic prediction using the one-step blending method in Chinese Holstein cattle.

Findings

Three methods, GBLUP (genomic best linear unbiased prediction), original one-step blending with a genomic relationship matrix, and adjusted one-step blending with an adjusted genomic relationship matrix, were compared with respect to the accuracy of genomic prediction for five milk production traits in Chinese Holstein. For the two one-step blending methods, de-regressed proofs of 17 509 non-genotyped cows, including 424 dams and 17 085 half-sisters of the validation cows, were incorporated in the prediction model. The results showed that, averaged over the five milk production traits, the one-step blending increased the accuracy of genomic prediction by about 0.12 compared to GBLUP. No further improvement in accuracies was obtained from the adjusted one-step blending over the original one-step blending in our situation. Improvements in accuracies obtained with both one-step blending methods were almost completely contributed by the non-genotyped dams.

Conclusions

Compared with GBLUP, the one-step blending approach can significantly improve the accuracy of genomic prediction for milk production traits in Chinese Holstein cattle. Thus, the one-step blending is a promising approach for practical genomic selection in Chinese Holstein cattle, where the reference population mainly consists of cows.     

Genome wide CNV analysis reveals additional variants associated with milk production traits in Holsteins

Background

Milk production is an economically important sector of global agriculture. Much attention has been paid to the identification of quantitative trait loci (QTL) associated with milk, fat, and protein yield and the genetic and molecular mechanisms underlying them. Copy number variation (CNV) is an emerging class of variants which may be associated with complex traits.

Results

In this study, we performed a genome-wide association between CNVs and milk production traits in 26,362 Holstein bulls and cows. A total of 99 candidate CNVs were identified using Illumina BovineSNP50 array data, and association tests for each production trait were performed using a linear regression analysis with PCA correlation. A total of 34 CNVs on 22 chromosomes were significantly associated with at least one milk production trait after false discovery rate (FDR) correction. Some of those CNVs were located within or near known QTL for milk production traits. We further investigated the relationship between associated CNVs with neighboring SNPs. For all 82 combinations of traits and CNVs (less than 400 kb in length), we found 17 cases where CNVs directly overlapped with tag SNPs and 40 cases where CNVs were adjacent to tag SNPs. In 5 cases, CNVs located were in strong linkage disequilibrium with tag SNPs, either within or adjacent to the same haplotype block. There were an additional 20 cases where CNVs did not have a significant association with SNPs, suggesting that the effects of those CNVs were probably not captured by tag SNPs.

Conclusion

We conclude that combining CNV with SNP analyses reveals more genetic variations underlying milk production traits than those revealed by SNPs alone.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-683) contains supplementary material, which is available to authorized users.     

Identification of genomic regions associated with inbreeding depression in Holstein and Jersey dairy cattle

Background

Inbreeding reduces the fitness of individuals by increasing the frequency of homozygous deleterious recessive alleles. Some insight into the genetic architecture of fitness, and other complex traits, can be gained by using single nucleotide polymorphism (SNP) data to identify regions of the genome which lead to reduction in performance when identical by descent (IBD). Here, we compared the effect of genome-wide and location-specific homozygosity on fertility and milk production traits in dairy cattle.

Methods

Genotype data from more than 43 000 SNPs were available for 8853 Holstein and 4138 Jersey dairy cows that were part of a much larger dataset that had pedigree records (338 696 Holstein and 64 049 Jersey animals). Measures of inbreeding were based on: (1) pedigree data; (2) genotypes to determine the realised proportion of the genome that is IBD; (3) the proportion of the total genome that is homozygous and (4) runs of homozygosity (ROH) which are stretches of the genome that are homozygous.

Results

A 1% increase in inbreeding based either on pedigree or genomic data was associated with a decrease in milk, fat and protein yields of around 0.4 to 0.6% of the phenotypic mean, and an increase in calving interval (i.e. a deterioration in fertility) of 0.02 to 0.05% of the phenotypic mean. A genome-wide association study using ROH of more than 50 SNPs revealed genomic regions that resulted in depression of up to 12.5 d and 260 L for calving interval and milk yield, respectively, when completely homozygous.

Conclusions

Genomic measures can be used instead of pedigree-based inbreeding to estimate inbreeding depression. Both the diagonal elements of the genomic relationship matrix and the proportion of homozygous SNPs can be used to measure inbreeding. Longer ROH (>3 Mb) were found to be associated with a reduction in milk yield and captured recent inbreeding independently and in addition to overall homozygosity. Inbreeding depression can be reduced by minimizing overall inbreeding but maybe also by avoiding the production of offspring that are homozygous for deleterious alleles at specific genomic regions that are associated with inbreeding depression.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0071-7) contains supplementary material, which is available to authorized users.     

Use of partial least squares regression to impute SNP genotypes in Italian Cattle breeds

Background

The objective of the present study was to test the ability of the partial least squares regression technique to impute genotypes from low density single nucleotide polymorphisms (SNP) panels i.e. 3K or 7K to a high density panel with 50K SNP. No pedigree information was used.

Methods

Data consisted of 2093 Holstein, 749 Brown Swiss and 479 Simmental bulls genotyped with the Illumina 50K Beadchip. First, a single-breed approach was applied by using only data from Holstein animals. Then, to enlarge the training population, data from the three breeds were combined and a multi-breed analysis was performed. Accuracies of genotypes imputed using the partial least squares regression method were compared with those obtained by using the Beagle software. The impact of genotype imputation on breeding value prediction was evaluated for milk yield, fat content and protein content.

Results

In the single-breed approach, the accuracy of imputation using partial least squares regression was around 90 and 94% for the 3K and 7K platforms, respectively; corresponding accuracies obtained with Beagle were around 85% and 90%. Moreover, computing time required by the partial least squares regression method was on average around 10 times lower than computing time required by Beagle. Using the partial least squares regression method in the multi-breed resulted in lower imputation accuracies than using single-breed data. The impact of the SNP-genotype imputation on the accuracy of direct genomic breeding values was small. The correlation between estimates of genetic merit obtained by using imputed versus actual genotypes was around 0.96 for the 7K chip.

Conclusions

Results of the present work suggested that the partial least squares regression imputation method could be useful to impute SNP genotypes when pedigree information is not available.     

Whole-genome sequence analysis reveals differences in population management and selection of European low-input pig breeds

Background

A major concern in conservation genetics is to maintain the genetic diversity of populations. Genetic variation in livestock species is threatened by the progressive marginalisation of local breeds in benefit of high-output pigs worldwide. We used high-density SNP and re-sequencing data to assess genetic diversity of local pig breeds from Europe. In addition, we re-sequenced pigs from commercial breeds to identify potential candidate mutations responsible for phenotypic divergence among these groups of breeds.

Results

Our results point out some local breeds with low genetic diversity, whose genome shows a high proportion of regions of homozygosis (>50%) and that harbour a large number of potentially damaging mutations. We also observed a high correlation between genetic diversity estimates using high-density SNP data and Next Generation Sequencing data (r = 0.96 at individual level). The study of non-synonymous SNPs that were fixed in commercial breeds and also in any local breed, but with different allele, revealed 99 non-synonymous SNPs affecting 65 genes. Candidate mutations that may underlie differences in the adaptation to the environment were exemplified by the genes AZGP1 and TAS2R40. We also observed that highly productive breeds may have lost advantageous genotypes within genes involve in immune response – e.g. IL12RB2 and STAB1–, probably as a result of strong artificial in the intensive production systems in pig.

Conclusions

The high correlation between genetic diversity computed with the 60K SNP and whole genome re-sequence data indicates that the Porcine 60K SNP Beadchip provides reliable estimates of genomic diversity in European pig populations despite the expected bias. Moreover, this analysis gave insights for strategies to the genetic characterization of local breeds. The comparison between re-sequenced local pigs and re-sequenced commercial pigs made it possible to report candidate mutations to be responsible for phenotypic divergence among those groups of breeds. This study highlights the importance of low input breeds as a valuable genetic reservoir for the pig production industry. However, the high levels of ROHs, inbreeding and potentially damaging mutations emphasize the importance of the genetic characterization of local breeds to preserve their genomic variability.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-601) contains supplementary material, which is available to authorized users.     

Genome Scan for Locus Involved in Mandibular Prognathism in Pedigrees from China

Background

It is well known that genetic components play an important role in the etiology of mandibular prognathism, but few susceptibility loci have been mapped.

Methodology

In order to identify linkage regions for mandibular prognathism, we analyzed two Chinese pedigrees with 6,090 genome-wide single-nucleotide polymorphism (SNP) markers from Illumina Linkage-12 DNA Analysis Kit (average spacing 0.58 cM). Multipoint parametric and non-parametric (model-free) linkage analyses were used for the pedigrees.

Principal Finding

The most statistically significant linkage results were with markers on chromosome 4 (LOD  = 3.166 and NPL = 3.65 with rs 875864, 4p16.1, 8.38 cM). Candidate genes within the 4p16.1 include EVC, EVC2.

Conclusion

We detected a novel suggestive linkage locus for mandibular prognathism in two Chinese pedigrees, and this linkage region provides target for susceptibility gene identification, a process that will provide important insights into the molecular and cellular basis of mandibular prognathism.     

Strategies for imputation to whole genome sequence using a single or multi-breed reference population in cattle

Background

The advent of low cost next generation sequencing has made it possible to sequence a large number of dairy and beef bulls which can be used as a reference for imputation of whole genome sequence data. The aim of this study was to investigate the accuracy and speed of imputation from a high density SNP marker panel to whole genome sequence level. Data contained 132 Holstein, 42 Jersey, 52 Nordic Red and 16 Brown Swiss bulls with whole genome sequence data; 16 Holstein, 27 Jersey and 29 Nordic Reds had previously been typed with the bovine high density SNP panel and were used for validation. We investigated the effect of enlarging the reference population by combining data across breeds on the accuracy of imputation, and the accuracy and speed of both IMPUTE2 and BEAGLE using either genotype probability reference data or pre-phased reference data. All analyses were done on Bovine autosome 29 using 387,436 bi-allelic variants and 13,612 SNP markers from the bovine HD panel.

Results

A combined breed reference population led to higher imputation accuracies than did a single breed reference. The highest accuracy of imputation for all three test breeds was achieved when using BEAGLE with un-phased reference data (mean genotype correlations of 0.90, 0.89 and 0.87 for Holstein, Jersey and Nordic Red respectively) but IMPUTE2 with un-phased reference data gave similar accuracies for Holsteins and Nordic Red. Pre-phasing the reference data only lead to a minor decrease in the imputation accuracy, but gave a large improvement in computation time. Pre-phasing with BEAGLE was substantially faster than pre-phasing with SHAPEIT2 (2.5 hours vs. 52 hours for 242 individuals), and imputation with pre-phased data was faster in IMPUTE2 than in BEAGLE (5 minutes vs. 50 minutes per individual).

Conclusion

Combining reference populations across breeds is a good option to increase the size of the reference data and in turn the accuracy of imputation when only few animals are available. Pre-phasing the reference data only slightly decreases the accuracy but gives substantial improvements in speed. Using BEAGLE for pre-phasing and IMPUTE2 for imputation is a fast and accurate strategy.     

Efficient Method of Genotyping Ob/Ob Mice Using High Resolution Melting Analysis

Objective

Direct health care costs of obesity continue to grow throughout the world and research on obesity disease models are on the rise. The ob/ob mouse is a well-characterized model of obesity and associated risk factors. Successful breeding and backcrossing onto different backgrounds are essential to create knockout models. Ob/ob mice are sterile and heterozygotes must be identified by genotyping to maintain breeding colonies. Several methods are employed to detect the ob mutant allele, a single nucleotide polymorphism (SNP). Gel based methods are time consuming and inconsistent, and non-gel based assays rely upon expensive and complex reagents or instruments. A fast, high-throughput, cost effective, and consistent method to identify Lep^ob mutation is much needed.

Design and Methods

Primers to produce an amplicon for High Resolution Melting Analysis (HRM) of the Lep^ob SNP were designed and validated.

Results

Fluorescence normalized high resolution melting curve plots delineated ob/+, ob/ob, and WT genotypes. Genotypes were also confirmed phenotypically.

Conclusions

HRM of the Lep^ob SNP allows closed-tube identification of the Lep^ob mutation using a real-time PCR machine now common to most labs/departments. Advantages of this method include assay sensitivity/accuracy, low cost dyes, less optimization, and cost effectiveness as compared to other genotyping techniques.     

Differences in candidate gene association between European ancestry and African American asthmatic children

Background

Candidate gene case-control studies have identified several single nucleotide polymorphisms (SNPs) that are associated with asthma susceptibility. Most of these studies have been restricted to evaluations of specific SNPs within a single gene and within populations from European ancestry. Recently, there is increasing interest in understanding racial differences in genetic risk associated with childhood asthma. Our aim was to compare association patterns of asthma candidate genes between children of European and African ancestry.

Methodology/Principal Findings

Using a custom-designed Illumina SNP array, we genotyped 1,485 children within the Greater Cincinnati Pediatric Clinic Repository and Cincinnati Genomic Control Cohort for 259 SNPs in 28 genes and evaluated their associations with asthma. We identified 14 SNPs located in 6 genes that were significantly associated (p-values <0.05) with childhood asthma in African Americans. Among Caucasians, 13 SNPs in 5 genes were associated with childhood asthma. Two SNPs in IL4 were associated with asthma in both races (p-values <0.05). Gene-gene interaction studies identified race specific sets of genes that best discriminate between asthmatic children and non-allergic controls.

Conclusions/Significance

We identified IL4 as having a role in asthma susceptibility in both African American and Caucasian children. However, while IL4 SNPs were associated with asthma in asthmatic children with European and African ancestry, the relative contributions of the most replicated asthma-associated SNPs varied by ancestry. These data provides valuable insights into the pathways that may predispose to asthma in individuals with European vs. African ancestry.     

Predicting haplotype carriers from SNP genotypes in Bos taurus through linear discriminant analysis

Background

SNP (single nucleotide polymorphisms) genotype data are increasingly available in cattle populations and, among other things, can be used to predict carriers of specific haplotypes. It is therefore convenient to have a practical statistical method for the accurate classification of individuals into carriers and non-carriers. In this paper, we present a procedure combining variable selection (i.e. the selection of predictive SNPs) and linear discriminant analysis for the identification of carriers of a haplotype on BTA19 (Bos taurus autosome 19) known to be associated with reduced cow fertility. A population of 3645 Brown Swiss cows and bulls genotyped with the 54K SNP-chip was available for the analysis.

Results

The overall error rate for the prediction of haplotype carriers was on average very low (∼≤1%). 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 BTA19. The minimum set of SNPs to still achieve accurate predictions was 5, with a total test error rate of 1.59.

Conclusions

The 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.