Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure

Nianjun Liu,Cheongeun Oh,Liang Chen,Shuang Wang,Hongyu Zhao

doi:10.1186/1471-2156-6-s1-s26

Abstract

Single-nucleotide polymorphisms (SNPs) are a class of attractive genetic markers for population genetic studies and for identifying genetic variations underlying complex traits. However, the usefulness and efficiency of SNPs in comparison to microsatellites in different scientific contexts, e.g., population structure inference or association analysis, still must be systematically evaluated through large empirical studies. In this article, we use the Collaborative Studies on Genetics of Alcoholism (COGA) data from Genetic Analysis Workshop 14 (GAW14) to compare the performance of microsatellites and SNPs in the whole human genome in the context of population structure inference. A total of 328 microsatellites and 15,840 SNPs are used to infer population structure in 236 unrelated individuals. We find that, on average, the informativeness of random microsatellites is four to twelve times that of random SNPs for various population comparisons, which is consistent with previous studies. Our results also indicate that for the combined set of microsatellites and SNPs, SNPs constitute the majority among the most informative markers and the use of these SNPs leads to better inference of population structure than the use of microsatellites. We also find that the inclusion of less informative markers may add noise and worsen the results.

Highlights

Population structure inference from genetic markers is very important in a variety of contexts, such as in admixture and association mapping, evolutionary studies, forensics, medical risk prediction, and wildlife management [1,2,3,4,5]
Statistical methods have been proposed for population structure inference using multilocus genotypes [1,3,5,6] and have been widely used in practice [2,3,5,7]
Single-nucleotide polymorphisms (SNPs) represent the majority among the most informative markers. This is contrary to Rosenberg's observation [4] that "highly informative loci constitute a greater fraction of microsatellites than of SNPs"

Summary

Introduction

Population structure inference from genetic markers is very important in a variety of contexts, such as in admixture and association mapping, evolutionary studies, forensics, medical risk prediction, and wildlife management [1,2,3,4,5]. Single-nucleotide polymorphisms (SNPs) are a class of attractive genetic markers for population genetic studies and for identifying genetic variations underlying complex traits. This is because SNPs are highly abundant, functionally relevant, have relatively low mutation rates, and offer more rapid and highly automated genotyping. The informativeness is defined as [4]:

Methods

Results

Conclusion