Patterns of linkage disequilibrium and association mapping in diploid alfalfa (M. sativa L.)

Abstract

Association mapping enables the detection of marker-trait associations in unstructured populations by taking advantage of historical linkage disequilibrium (LD) that exists between a marker and the true causative polymorphism of the trait phenotype. Our first objective was to understand the pattern of LD decay in the diploid alfalfa genome. We used 89 highly polymorphic SSR loci in 374 unimproved diploid alfalfa (Medicago sativa L.) genotypes from 120 accessions to infer chromosome-wide patterns of LD. We also sequenced four lignin biosynthesis candidate genes (caffeoyl-CoA 3-O-methyltransferase (CCoAoMT), ferulate-5-hydroxylase (F5H), caffeic acid-O-methyltransferase (COMT), and phenylalanine amonialyase (PAL 1)) to identify single nucleotide polymorphisms (SNPs) and infer within gene estimates of LD. As the second objective of this study, we conducted association mapping for cell wall components and agronomic traits using the SSR markers and SNPs from the four candidate genes. We found very little LD among SSR markers implying limited value for genomewide association studies. In contrast, within gene LD decayed within 300 bp below an r (2) of 0.2 in three of four candidate genes. We identified one SSR and two highly significant SNPs associated with biomass yield. Based on our results, focusing association mapping on candidate gene sequences will be necessary until a dense set of genome-wide markers is available for alfalfa.

Fig. 1

Physical locations of 58 of 89 SSR markers and four candidate genes on Medicago truncatula chromosomes, Mt genome sequence version 3.5.1. The ruler indicates scale in Mb

Fig. 2

Depiction of candidate genes with overlapping amplicons comprising seven contigs illustrated. Representation of four candidate genes and overlapping amplicons (length of amplicons in bp listed in Supplemental Table 1). Exons are rectangles. Brackets indicate the size of unsequenced gene. Three filled diamonds indicate introns and sizes determined from M. truncatula genome sequence that were not included in the sequencing. Dagger indicates location of SNPs significantly associated with biomass yield

Fig. 3

Determining optimal value of K using the ad hoc procedure described by Pritchard et al. (2000) for 374 genotypes from 120 accessions (blue) and for a sub set of 72 accessions (red) (colour figure online)

Fig. 4

Plots of linkage disequilibrium (−log(Q value)) between SSR locus pairs on the same chromosome against their physical distance in Mb, based on the M. truncatula genome sequence, in five diploid alfalfa populations and over all 120 accessions

Fig. 5

Minor allele frequencies of 194 SNP discovered in seven contigs of the four candidate genes sequenced

Fig. 6

LD plots of squared correlations of allele frequencies (r ²) against distance between polymorphic sites for four candidate genes in seven contigs as depicted in Fig. 2

Fig. 7

Significant SNP-trait associations (a) CCoAoMT SNP 111 and yield 2007. Phenotypic effects of genotypes at position 111. Percentage of individuals sampled with each genotypic class for each subspecies and overall subspecies. b Significant SNP-trait association between F5H exon 2 SNP 276 and yield 2008. Phenotypic effects of genotypes at position 276. Percentage of individuals sampled with each genotypic class for each subspecies and overall subspecies