A comparison of SNP and STR loci for delineating population structure and performing individual genetic assignment

• Published in: BMC Genetics
• Main Authors: Glover, Kevin A., Hansen, Michael Møller, Lien, Sigbjørn, Als, Thomas Damm, Høyheim, Bjørn, Skaala, Øystein
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2010
• Subjects: Atlantic salmon
• Online Access: https://orbit.dtu.dk/en/publications/aa44f1d8-c1fb-434d-8069-fd4d832a6277; https://doi.org/10.1186/1471-2156-11-2; https://backend.orbit.dtu.dk/ws/files/10870454/0E2A9d01.pdf

Technological advances have lead to the rapid increase in availability of single nucleotide polymorphisms (SNPs) in a range of organisms, and there is a general optimism that SNPs will become the marker of choice for a range of evolutionary applications. Here, comparisons between 300 polymorphic SNPs and 14 short tandem repeats (STRs) were conducted on a data set consisting of approximately 500 Atlantic salmon arranged in 10 samples/populations. Results Global FST ranged from 0.033-0.115 and -0.002-0.316 for the 14 STR and 300 SNP loci respectively. Global FST was similar among 28 linkage groups when averaging data from mapped SNPs. With the exception of selecting a panel of SNPs taking the locus displaying the highest global FST for each of the 28 linkage groups, which inflated estimation of genetic differentiation among the samples, inferred genetic relationships were highly similar between SNP and STR data sets and variants thereof. The best 15 SNPs (30 alleles) gave a similar level of self-assignment to the best 4 STR loci (83 alleles), however, addition of further STR loci did not lead to a notable increase assignment whereas addition of up to 100 SNP loci increased assignment. Conclusion Whilst the optimal combinations of SNPs identified in this study are linked to the samples from which they were selected, this study demonstrates that identification of highly informative SNP loci from larger panels will provide researchers with a powerful approach to delineate genetic relationships at the individual and population levels.