Genomic Selection Analysis Research Articles

Analytical and statistical consideration on the use of the ISAG-ICAR-SNP bovine panel for parentage control, using the Illumina BeadChip technology: example on the German Holstein population.

BackgroundParentage control is moving from short tandem repeats- to single nucleotide polymorphism (SNP) systems. For SNP-based parentage control in cattle, the ISAG-ICAR Committee proposes a set of 100/200 SNPs but quality criteria are lacking. Regarding German Holstein-Friesian cattle with only a limited number of evaluated individuals, the exclusion probability is not well-defined. We propose a statistical procedure for excluding single SNPs from parentage control, based on case-by-case evaluation of the GenCall score, to minimize parentage exclusion, based on miscalled genotypes. Exclusion power of the ISAG-ICAR SNPs used for the German Holstein-Friesian population was adjusted based on the results of more than 25 000 individuals.ResultsExperimental data were derived from routine genomic selection analyses of the German Holstein-Friesian population using the Illumina BovineSNP50 v2 BeadChip (20 000 individuals) or the EuroG10K variant (7000 individuals). Averages and standard deviations of GenCall scores for the 200 SNPs of the ISAG-ICAR recommended panel were calculated and used to calculate the downward Z-value. Based on minor allelic frequencies in the Holstein-Friesian population, one minus exclusion probability was equal to 1.4×10−10 and 7.2×10−26, with one and two parents, respectively. Two monomorphic SNPs from the 100-SNP ISAG-ICAR core-panel did not contribute. Simulation of 10 000 parentage control combinations, using the GenCall score data from both BeadChips, showed that with a Z-value greater than 3.66 only about 2.5% parentages were excluded, based on the ISAG-ICAR recommendations (core-panel: ≥ 90 SNPs for one, ≥ 85 SNPs for two parents). When applied to real data from 1750 single parentage assessments, the optimal threshold was determined to be Z = 5.0, with only 34 censored cases and reduction to four (0.2%) doubtful parentages. About 70 parentage exclusions due to weak genotype calls were avoided, whereas true exclusions (n = 34) were unaffected.ConclusionsUsing SNPs for parentage evaluation provides a high exclusion power also for parent identification. SNPs with a low GenCall score show a high tendency towards intra-molecular secondary structures and substantially contribute to false exclusion of parentages. We propose a method that controls this error without excluding too many parent combinations from the evaluation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-014-0085-1) contains supplementary material, which is available to authorized users.

Genetic control of canine leishmaniasis: genome-wide association study and genomic selection analysis.

BackgroundThe current disease model for leishmaniasis suggests that only a proportion of infected individuals develop clinical disease, while others are asymptomatically infected due to immune control of infection. The factors that determine whether individuals progress to clinical disease following Leishmania infection are unclear, although previous studies suggest a role for host genetics. Our hypothesis was that canine leishmaniasis is a complex disease with multiple loci responsible for the progression of the disease from Leishmania infection.Methodology/Principal FindingsGenome-wide association and genomic selection approaches were applied to a population-based case-control dataset of 219 dogs from a single breed (Boxer) genotyped for ∼170,000 SNPs. Firstly, we aimed to identify individual disease loci; secondly, we quantified the genetic component of the observed phenotypic variance; and thirdly, we tested whether genome-wide SNP data could accurately predict the disease.Conclusions/SignificanceWe estimated that a substantial proportion of the genome is affecting the trait and that its heritability could be as high as 60%. Using the genome-wide association approach, the strongest associations were on chromosomes 1, 4 and 20, although none of these were statistically significant at a genome-wide level and after correcting for genetic stratification and lifestyle. Amongst these associations, chromosome 4: 61.2–76.9 Mb maps to a locus that has previously been associated with host susceptibility to human and murine leishmaniasis, and genomic selection estimated markers in this region to have the greatest effect on the phenotype. We therefore propose these regions as candidates for replication studies. An important finding of this study was the significant predictive value from using the genomic information. We found that the phenotype could be predicted with an accuracy of ∼0.29 in new samples and that the affection status was correctly predicted in 60% of dogs, significantly higher than expected by chance, and with satisfactory sensitivity-specificity values (AUC = 0.63).

Short communication: Effect of mutation age on genomic predictions

Genomic selection relies on the whole-genome evaluation of single nucleotide polymorphisms (SNP), some of them linked to quantitative trait loci (QTL). Although statistical methodology has been developed for the analysis of genomic data, little is known about the performance of SNP association studies when trying to capture variability from QTL mutations of different ages. Within this context, the influence of mutation age was analyzed under a simulation design, assuming presence or absence of selection on mutant QTL alleles. Focusing on a unique chromosome with a single QTL located in the proximal end, the performance of the genomic selection analyses was evaluated in terms of standardized mean square error (MSE). For all simulation scenarios, MSE was highest for the youngest mutations. The MSE was progressively reduced with mutation age under random mating and soft selection, and reached its maximum performance with the oldest mutations. On the other hand, moderate and strong selection caused a quick reduction of the MSE from youngest mutations to mutations arising in generations 920 to 939, thus resulting in a progressive increase for older mutations. In both cases, very young mutations escaped from genomic selection analyses, releasing a relevant amount of genetic variability that could not be captured and used in genomic selection programs. This demonstrated the need for new analytical approaches to model relevant and recent sources of variation; if captured, these young mutations could substantially contribute to current breeding schemes.