Use of SNP genotypes to identify carriers of harmful recessive mutations in cattle populations

Abstract

BackgroundSNP (single nucleotide polymorphisms) genotype data are increasingly available in cattle populations and, among other things, can be used to predict carriers of specific mutations. 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 compared – through cross-validation– five classification models (Lasso-penalized logistic regression –Lasso, Support Vector Machines with either linear or radial kernel –SVML and SVMR, k-nearest neighbors –KNN, and multi-allelic gene prediction –MAG), for the identification of carriers of the TUBD1 recessive mutation on BTA19 (Bos taurus autosome 19), known to be associated with high calf mortality. A population of 3116 Fleckvieh and 392 Brown Swiss animals genotyped with the 54K SNP-chip was available for the analysis.ResultsIn general, the use of SNP genotypes proved to be very effective for the identification of mutation carriers. The best predictive models were Lasso, SVML and MAG, with an average error rate, respectively, of 0.2 %, 0.4 % and 0.6 % in Fleckvieh, and 1.2 %, 0.9 % and 1.7 % in Brown Swiss. For the three models, the false positive rate was, respectively, 0.1 %, 0.1 % and 0.2 % in Fleckvieh, and 3.0 %, 2.4 % and 1.6 % in Brown Swiss; the false negative rate was 4.4 %, 7.6 %1.0 % in Fleckvieh, and 0.0 %, 0.1% and 0.8 % in Brown Swiss. MAG appeared to be more robust to sample size reduction: with 25 % of the data, the average error rate was 0.7 % and 2.2 % in Fleckvieh and Brown Swiss, compared to 2.1 % and 5.5 % with Lasso, and 2.6 % and 12.0 % with SVML.ConclusionsThe use of SNP genotypes is a very effective and efficient technique for the identification of mutation carriers in cattle populations. Very few misclassifications were observed, overall and both in the carriers and non-carriers classes. This indicates that this is a very reliable approach for potential applications in cattle breeding.

Combining micro-RNA and protein sequencing to detect robust biomarkers for Graves’ disease and orbitopathy

“…Combining results from inference and predictions, especially if leveraging data resampling techniques, is a powerful approach to detecting more robust signals of associations. In the field of genomics, combining results from GWAS and from genomic predictions has been proposed to address the issue of spurious genetic marker-phenotype associations and produce more meaningful results 47 – 49 or, reverting perspective, to increase the accuracy of genomic predictions 50 . Especially if the accuracy of predictions is high, selected variables from predictive models are likely to play a role in the studied phenotype, and can effectively contribute to identifying robust biomarkers associated with the disease.…”

Symposium review: Genetics, genome-wide association study, and genetic improvement of dairy fertility traits

“…These positive responses to selection on fertility traits support the efficiency of genomic selection, even for low-heritable traits. In addition, since the recessive lethal haplotypes were first identified by VanRaden et al (2011), these deleterious mutations have been routinely scanned and removed from the dairy population (Cooper et al, 2014;Biffani et al, 2015;Biscarini et al, 2016;Schütz et al, 2016), which can help further increase the fertility of dairy cattle. Despite the success of genomic selection in improving economically important traits in dairy cattle, there are potential negative effects of genomic selection, including the hindrance of developing resilient cattle and loss of diversity, that may negatively affect the long-term selection responses (Notter, 1999;Hayes et al, 2009).…”

Genetic Improvement in South African Livestock: Can Genomics Bridge the Gap Between the Developed and Developing Sectors?

“…Over the past two decades, both microsatellite and SNP markers have contributed to the development of diagnostic testing of genetic defects and DNA-based parentage (Van Marle-Koster et al, 2013 ). SNP arrays are widely applied in routine genotyping for genomic selection in several farm animal species providing an added advantage of using these genotypes for detection and prediction of carriers of genetic defects (Biscarini et al, 2016 ). Different methods have been reported for prediction that include haplotype-based predictions (Pirola et al, 2013 ) and discriminant analyses (Biffani et al, 2015 ).…”

“…Different methods have been reported for prediction that include haplotype-based predictions (Pirola et al, 2013 ) and discriminant analyses (Biffani et al, 2015 ). Studies have shown that the accuracy of prediction for the genetic defects could be comparable when using genotypes generated with lower density (Bovine LD) versus a higher density 54K Bovine SNP array (Biscarini et al, 2016 ). The availability of genotypes furthermore provide the potential for identification of beneficial genes such as the Celtic variant of the POLLED gene for homozygous polled animals (Medugorac et al, 2012 ).…”