Population structure and linkage disequilibrium in oat (Avena sativa L.): implications for genome-wide association studies

Theor Appl Genet. 2011 Feb;122(3):623-32. doi: 10.1007/s00122-010-1474-7. Epub 2010 Nov 2.

Abstract

The level of population structure and the extent of linkage disequilibrium (LD) can have large impacts on the power, resolution, and design of genome-wide association studies (GWAS) in plants. Until recently, the topics of LD and population structure have not been explored in oat due to the lack of a high-throughput, high-density marker system. The objectives of this research were to survey the level of population structure and the extent of LD in oat germplasm and determine their implications for GWAS. In total, 1,205 lines and 402 diversity array technology (DArT) markers were used to explore population structure. Principal component analysis and model-based cluster analysis of these data indicated that, for the lines used in this study, relatively weak population structure exists. To explore LD decay, map distances of 2,225 linked DArT marker pairs were compared with LD (estimated as r²). Results showed that LD between linked markers decayed rapidly to r² = 0.2 for marker pairs with a map distance of 1.0 centi-Morgan (cM). For GWAS, we suggest a minimum of one marker every cM, but higher densities of markers should increase marker-QTL association and therefore detection power. Additionally, it was found that LD was relatively consistent across the majority of germplasm clusters. These findings suggest that GWAS in oat can include germplasm with diverse origins and backgrounds. The results from this research demonstrate the feasibility of GWAS and related analyses in oat.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Avena / genetics*
  • Cluster Analysis
  • Genetic Markers
  • Genome-Wide Association Study*
  • Linkage Disequilibrium / genetics*
  • Molecular Sequence Annotation
  • Population Dynamics
  • Principal Component Analysis
  • Seeds / genetics

Substances

  • Genetic Markers