DOI: 10.5061/dryad.pq143
title: Data from: Candidate genes and genetic architecture of symbiotic and agronomic traits revealed by whole-genome, sequence-based association genetics in Medicago truncatula

description: Genome-wide association study (GWAS) has revolutionized the search for the
 genetic basis of complex traits. To date, GWAS have generally relied on
 relatively sparse sampling of nucleotide diversity, which is likely to
 bias results by preferentially sampling high-frequency SNPs not in
 complete linkage disequilibrium (LD) with causative SNPs. To avoid these
 limitations we conducted GWAS with &gt;6 million SNPs identified by
 sequencing the genomes of 226 accessions of the model legume Medicago
 truncatula. We used these data to identify candidate genes and the genetic
 architecture underlying phenotypic variation in plant height, trichome
 density, flowering time, and nodulation. The characteristics of candidate
 SNPs differed among traits, with candidates for flowering time and
 trichome density in distinct clusters of high linkage disequilibrium (LD)
 and the minor allele frequencies (MAF) of candidates underlying variation
 in flowering time and height significantly greater than MAF of candidates
 underlying variation in other traits. Candidate SNPs tagged several
 characterized genes including nodulation related genes SERK2, MtnodGRP3,
 MtMMPL1, NFP, CaML3, MtnodGRP3A and flowering time gene MtFD as well as
 uncharacterized genes that become candidates for further molecular
 characterization. By comparing sequence-based candidates to candidates
 identified by in silico 250K SNP arrays, we provide an empirical example
 of how reliance on even high-density reduced representation genomic makers
 can bias GWAS results. Depending on the trait, only 30–70% of the top 20
 in silico array candidates were within 1 kb of sequence-based candidates.
 Moreover, the sequence-based candidates tagged by array candidates were
 heavily biased towards common variants; these comparisons underscore the
 need for caution when interpreting results from GWAS conducted with
 sparsely covered genomes.
description: StantonGeddes2013_Medicago_plant_dataSpreadsheet of plant phenotype data collected in greenhouse experiment for genome-wide association study. Column headings are: block, pot (individual plant ID), trtmnt (rhizobia treatment applied to the plant, either "rhz_12" for a mixture of two rhizobia strains, or "control" for no rhizobia), HM_accession (Medicago HapMap accession), height_1 (height recorded at about two weeks), leaves_1 (number of leaves at about two weeks), height_2 (intermediate height measurement), height_3 (final height before harvest), branch_3 (number of branches on plant before harvest), flowering date (date first flower observed), nodule_above (number of nodules counted in top 5 cm of roots), nodule_below (number of nodules below 5 cm of root growth). More information is available in the R markdown script "StantonGeddes2013_script.Rmd" that accompanies this file.R script for accession phenotypesR markdown script used to generate the accession means that were used in the genome-wide association analysis using the TASSEL program.StantonGeddes2013_script.RmdStantonGeddes2013_Medicago_leaf_dataSpreadsheet of plant leaf data collected by Mohammed Yakub for genome-wide association analysis. Columns are: pot (unique ID that corresponds to plants in "StantonGeddes2013_Medicago_plant_data.csv" file), Trichomes (number of trichomes in the 2 mm long area immediately below the leaf), width mm (width of the petiole in the area where trichomes were counted), petiole area (petiole width times 2 cm), leaf area (area estimated from leaf scan by ImageJ), leaf weight (weight of each leaf after at least 24 hours of drying)StantonGeddes2013_Medicago_nodule_occupancySpreadsheet of nodule occupancy data, described in more detail in README file.R script for nodule occupancy accession phenotypesR script to calculate rhizobia strain nodule occupancy for genome-wide association analysis.StantonGeddes2013_Medicago_nodule_occupancy_script.rREADME for Tassel analysisPDF file that describes how Tassel analysis was performed, and analysis of resulting data with R scriptsMtHap_GWA_README.pdfshell script for Tassel analysisPortable Bash System (PBS) shell script for Tassel analysis run on Minnesota Supercomputing Institute serverstassel_shell.pbsR script for Tassel analysisR script called by PBS shell script to analyse results of Tasseltassel_results.rR functions for Tassel analysisFunctions required by R for analysis of Tassel resultstassel_analysis_functions.r