SNP-haplotypes: An accurate approach for parentage and relatedness inference in gilthead sea bream (Sparus aurata)

Abstract

Pedigree information among individuals has become a key factor in the correct management and conservation of wild populations, as well as captive stocks. Despite the growing interest in the identification and application of single nucleotide polymorphism (SNP) markers, to date, microsatellite loci continues to be considered as the genetic marker par excellence to establish the degree of relationship among individuals. In fact, although high-density SNP panels are a highly accurate tool for inferring kinship relationships, at small-scale, with only a reduced number of SNPs involved, it is difficult to preserve that accuracy due to the bi-allelic nature of this genetic marker.The present study has evaluated the accuracy of SNP-haplotypes (genotypic combination of single SNPs located in the same gene) as parentage and relatedness tool in gilthead sea bream (Sparus aurata). Initially, by direct sequencing of 11 genes in a set of 30 unrelated individuals with heterogeneous origin, a total of 398 putative SNPs were identified and, by means of a filtering procedure to remove false positive and SNPs in a very low frequency, 164 SNPs were selected for the final panel. Using the iPLEX Gold MassARRAY technology, a subset of 58 SNPs located in 7 genes were genotyped in 424 individuals organized into two broodstocks (54 breeders and 370 offspring with pedigree previously inferred by microsatellite loci). From the obtained genotype data, a SNP-haplotype reconstruction was carried out for each gene. According to this approach, 58 biallelic single SNPs were turned into 7 multi-allelic markers (1 gene = 1 marker) whose allele (haplotype) number ranged from 3 to 41. This SNP-haplotypes resolved parentage assignment with an accuracy higher than 99%, resulting in a 10.5% and 3.5% more accuracy than the set of 58 SNPs and 9 microsatellite loci, respectively. Likewise, under a scenario in which ancestral information was not available, SNP-haplotypes showed an accuracy similar than microsatellite loci for discriminating unrelated individuals. Using this approach, a proportion close to 94% of unrelated individuals correctly classified for the coefficient of relationship equal to zero (theoretically expected relatedness value between unrelated individuals) was achieved. The 6% of individuals, misclassified as unrelated, were largely half-sibs since the full-sibs did not exceed 0.03%. In conclusion, we report herein that SNP-haplotypes approach represents a highly efficient alternative to microsatellite loci to infer parentage and assess relatedness.