Prediction of a deletion copy number variant by a dense SNP panel

A deletion type CNV on bovine chromosome 6 was predicted from its neighboring SNP with a multiple regression model. Our dataset consisted of CNV genotypes of 1,682 cows, along with 100 surrounding SNP genotypes. A prediction model was fitted considering 10 to 100 surrounding SNP and the accuracy obtained directly from the model was confirmed by cross-validation.The accuracy of prediction increased with an increasing number of SNP in the model and the predicted accuracies were similar to those obtained by cross-validation. A substantial increase in accuracy was observed when the number of SNP increased from 10 to 50 but thereafter the increase was smaller, reaching the highest accuracy (0.94) with 100 surrounding SNP. Thus, we conclude that the genotype of a deletion type CNV and its putative QTL effect can be predicted with a maximum accuracy of 0.94 from surrounding SNP. This high prediction accuracy suggests that genetic variation due to simple deletion CNV is well captured by dense SNP panels. Since genomic selection relies on the availability of a dense marker panel with markers in close linkage disequilibrium to the QTL in order to predict their genetic values, we also discuss opportunities for genomic selection to predict the effects of CNV by dense SNP panels, when CNV cause variation in quantitative traits.A recently recognized source of genomic structural variation called Copy Number Variation (CNV), is gaining interest in genomic studies. It is defined as a DNA segment that is 1 or more kb long and is present at a variable copy number in comparison with a reference genome [1]. CNV are shown to be functionally active in humans. They are responsible for phenotypic changes by altering gene dosage, disturbing coding sequences and perturbing long-range gene regulation [2]. With the discovery of CNV in the cattle genome [3-5] and their potential to cause variation in economically important traits, capturing the effects of CNV and other complex genotypes on pheno