GenoTriplo: A SNP genotype calling method for triploids

Abstract

Triploidy is very useful in both aquaculture and some cultivated plants as the induced sterility helps to enhance growth and product quality, as well as acting as a barrier against the contamination of wild populations by escapees. To use genetic information from triploids for academic or breeding purposes, an efficient and robust method to genotype triploids is needed. We developed such a method for genotype calling from SNP arrays, and we implemented it in the R package named GenoTriplo. Our method requires no prior information on cluster positions and remains unaffected by shifted luminescence signals. The method relies on starting the clustering algorithm with an initial higher number of groups than expected from the ploidy level of the samples, followed by merging groups that are too close to each other to be considered as distinct genotypes. Accurate classification of SNPs is achieved through multiple thresholds of quality controls. We compared the performance of GenoTriplo with that of fitPoly, the only published method for triploid SNP genotyping with a free software access. This was assessed by comparing the genotypes generated by both methods for a dataset of 1232 triploid rainbow trout genotyped for 38,033 SNPs. The two methods were consistent for 89% of the genotypes, but for 26% of the SNPs, they exhibited a discrepancy in the number of different genotypes identified. For these SNPs, GenoTriplo had >95% concordance with fitPoly when fitPoly genotyped better. On the contrary, when GenoTriplo genotyped better, fitPoly had less than 50% concordance with GenoTriplo. GenoTriplo was more robust with less genotyping errors. It is also efficient at identifying low-frequency genotypes in the sample set. Finally, we assessed parentage assignment based on GenoTriplo genotyping and observed significant differences in mismatch rates between the best and second-best couples, indicating high confidence in the results. GenoTriplo could also be used to genotype diploids as well as individuals with higher ploidy level by adjusting a few input parameters.

Fig 1. Algorithm stages for the clustering phase.

Fig 2. Mean contrast and distances between clusters from 1803 random markers (50.000 points on the graph).

Surrounded in blue, clusters that should be merged. Surrounded in green, clusters that should not be merged. The red line represents the separation between clusters that should and should not be merged. BBB/BBA stands for: mean contrast and distance between a supposed BBB cluster and BBA cluster.

Fig 3. Illustration of genotype determination for 1; 2 or 3; and 4 clusters identified for a given SNP.

Fig 4. Example of implementation of the additional step to account for highly shifted contrast signal.

Fig 5. Examples of distribution on the axes of contrast and signal strength of genotypes identified by GenoTriplo for each category of markers.

Fig 6. Mean contrast and signal strength values for genotypes of SNP with 1, 2, 3 and 4 different genotypes (from left to right) for fitPoly (above) and GenoTriplo (under) methods.

Fig 7. Number of offspring as a function of the number of mismatches for the best couple (blue) and the second-best couple (red) found by APIS parentage assignment.

Fig 8. Mean Contrast and SigStren value for each marker genotype given by either GenoTriplo or ploidyClassifier.