Application of R and Bioinformatics Tools for Analyses of Genetic Diversity and Population Stratification in Synthetic Hexaploid Wheat.

The presence of numerous software programs and R packages to investigate population structure, genetic diversity, and genome wide association analysis puts researchers in a dilemma of finding the right one while analyzing their data. Therefore, it is imperative to systematically analyze, document, and share the methodology with the researchers. In this presentation, we will share our experiences of using several software programs and R packages including STRUCTURE, fastStructure, StrAuto, TASSEL- GBS, cluster analysis, DAPC using adegenet 2.0 R package, PCA in R, MEGA, GeneAlex, Areleqin, etc. for population structure, genetic diversity, and genome-wide association studies. We will also highlight the need for the documentation and release of R scripts using Markdown, building web framework using Shiny Apps, and avenues for development of R-packages, as a solution toward developing a one-stop solution for the research community. In this study, synthetic wheat lines (139), produced from diverse accessions of tetraploid wheat (T. turgidum, 2n=28, AABB) and diploid goat grass (Aegiolops tauschii, 2n=14, DD), and two Turkish bread wheat cultivars were examined for genetic diversity and population stratification using genotype by sequencing (GBS) derived 37,592 single nucleotide polymorphisms (SNPs).Out of 139 synthetic lines, 15 of them (spring type) were developed by Kyoto University, Japan. These spring types were originated from one spring durum (Langdon)parent and 15 different accession of Ae. tauschii. Rest of these synthetic lines (winter type) were developed by CIMMYT. These lines originated from the six winter durum parents and 10 Ae .tauschii accessions from Azerbaijan, Iran, and Russia. The population structure and diversity within the subgroups were examined with analyses of variance using, PCA, STRUCTURE, DAPC etc. Three major sub-population of synthetics were identified using the program STRUCTURE and confirmed by PCA and cluster analysis, and the subgroups mainly differentiated by geographical location.

Keywords: SNPs, STRUCTURE, TASSEL-GBS, breeding, genomic data