Comparison of SSR and SNP markers in estimation of genetic diversity and population structure of Indian rice varieties

Abstract

Simple sequence repeat (SSR) and Single Nucleotide Polymorphic (SNP), the two most robust markers for identifying rice varieties were compared for assessment of genetic diversity and population structure. Total 375 varieties of rice from various regions of India archived at the Indian National GeneBank, NBPGR, New Delhi, were analyzed using thirty six genetic markers, each of hypervariable SSR (HvSSR) and SNP which were distributed across 12 rice chromosomes. A total of 80 alleles were amplified with the SSR markers with an average of 2.22 alleles per locus whereas, 72 alleles were amplified with SNP markers. Polymorphic information content (PIC) values for HvSSR ranged from 0.04 to 0.5 with an average of 0.25. In the case of SNP markers, PIC values ranged from 0.03 to 0.37 with an average of 0.23. Genetic relatedness among the varieties was studied; utilizing an unrooted tree all the genotypes were grouped into three major clusters with both SSR and SNP markers. Analysis of molecular variance (AMOVA) indicated that maximum diversity was partitioned between and within individual level but not between populations. Principal coordinate analysis (PCoA) with SSR markers showed that genotypes were uniformly distributed across the two axes with 13.33% of cumulative variation whereas, in case of SNP markers varieties were grouped into three broad groups across two axes with 45.20% of cumulative variation. Population structure were tested using K values from 1 to 20, but there was no clear population structure, therefore Ln(PD) derived Δk was plotted against the K to determine the number of populations. In case of SSR maximum Δk was at K=5 whereas, in case of SNP maximum Δk was found at K=15, suggesting that resolution of population was higher with SNP markers, but SSR were more efficient for diversity analysis.

Figure 1. Major allele frequency spectrum for (a) 36 SSR and (b) 36 SNPs in 375 rice varieties.

Figure 2. NJ tree constructed for (a) SSR and (b) SNP data based on regions south (red), north-east (sky blue), east (blue), north (green), west (pink) and CVR (orange).

Figure 3. Analysis of Molecular variance (AMOVA) of 375 rice varieties based on (a) SSR data and (b) SNP data.

Figure 4. Principal Coordinate Analysis (PCoA) of 375 rice varieties based on (a) SSR data and (b) SNP data.

Figure 5. Estimation of population using LnP(D) derived Δk for k from 1 to20, (a) SSRs and (b) SNPs.

Figure 6. Model based clustering of (a) SSR (K=5) and (b) SNP (K=15) of Indica rice cultivars, (boxes indicates distribution of Autumn rice, also known as 'Aus' in West Bengal, 'Ahu' in Assam, 'Beali' in Orissa, 'Bhadai' in Bihar, 'Virippu' in Kerala and 'Kuruvai/kar/ Sornavari' in Tamil Nadu).