Verification of Arabidopsis stock collections using SNPmatch, a tool for genotyping high-plexed samples

Abstract

Large-scale studies such as the Arabidopsis thaliana ‘1,001 Genomes’ Project require routine genotyping of stocks to avoid sample contamination. To genotype samples efficiently and economically, sequencing must be inexpensive and data processing simple. Here we present SNPmatch, a tool that identifies strains (or inbred lines, or accessions) by matching them to a SNP database. We tested the tool by performing low-coverage resequencing of over 2,000 strains from our lab seed stock collection. SNPmatch correctly genotyped samples from 1-fold coverage sequencing data, and could also identify the parents of F1 or F2 individuals. SNPmatch can be run either on the command line or through AraGeno (https://arageno.gmi.oeaw.ac.at), a web interface that permits sample genotyping from a user-uploaded VCF or BED file.

An ultra high-densityArabidopsis thalianacrossover map that refines the influences of structural variation and epigenetic features

“…Our library preparation method had a success rate of around 95% when processing hundreds of samples in under 3 hours. Furthermore, at a cost of~$2.50 per sample, this method is, as of writing this manuscript, highly cost effective and compares favorably in terms of speed with similar protocols (Baym et al 2015;Pisupati et al 2017) . A version of this protocol could also be used with homemade Tn5 transposome and buffer (Hennig et al 2018) .…”

In the presence of population structure: From genomics to candidate genes underlying local adaptation

“…To identify SNPs showing significant associations with climate, while accounting for population structure we used two prominent methods: GEMMA association (Zhou & Stephens, ) and LFMM (Caye et al, ), version 2. These methods were applied to four climate variables [Minimum Temperature of Coldest Month; Precipitation during Warmest Quarter; Soil moisture; and Photosynthetically Active Radiation during Fall (which is the time of germination for fall ecotypes such as in Italy and Sweden)] important to local adaptation (Lasky et al, ), and a SNP genotype matrix (1001 Genomes Consortium, ) derived from a set of 875 re‐sequenced Arabidopsis thaliana Eurasian accessions (Table ) that excluded laboratory escapees/contaminants (Pisupati et al, ) and accessions from outside the native Eurasian and African range of A. thaliana that may have weaker patterns of local adaptation (Lasky et al, ).…”

“…The two GEA methods (GEMMA and LFMM) were applied across a set of 875 Eurasian accessions (1001Genomes Consortium, 2016 and four climate variables covering temperature, precipitation and photosynthetically active radiation (Lasky et al, 2012;Price et al, 2018). The 875 accessions excluded likely laboratory escapees or contaminants (Pisupati et al, 2017) and invasive lines that may reduce the signal of local adaptation (Lasky et al, 2012). BAYESCAN on the other hand was applied to a sample of accessions in North Sweden and South Italy.…”

In the presence of population structure: From genomics to candidate genes underlying local adaptation

“…Using a SNP genotype matrix for a panel of 1,135 globally distributed accessions downloaded from the 1001 Genomes database, we filtered out accessions from outside the native Eurasian and North African range of A. thaliana, as these accessions may have weaker patterns of local adaptation (Lasky, et al 2012). We also filtered out accessions that were likely laboratory escapees or contaminants (Pisupati, et al 2017), leaving 875 accessions. After we filtered for biallelic SNPs with minor allele frequency >0.05, we tested association with home climate of ecotype and tested for potential confounding effects of population structure using the software "gemma" (Zhou and Stephens 2012).…”