The Impact of Genotyping-by-Sequencing Pipelines on SNP Discovery and Identification of Markers Associated with Verticillium Wilt Resistance in Autotetraploid Alfalfa (Medicago sativa L.)

Abstract

Verticillium wilt (VW) of alfalfa is a soilborne disease causing severe yield loss in alfalfa. To identify molecular markers associated with VW resistance, we used an integrated framework of genome-wide association study (GWAS) with high-throughput genotyping by sequencing (GBS) to identify loci associated with VW resistance in an F1 full-sib alfalfa population. Phenotyping was performed using manual inoculation of the pathogen to cloned plants of each individual and disease severity was scored using a standard scale. Genotyping was done by GBS, followed by genotype calling using three bioinformatics pipelines including the TASSEL-GBS pipeline (TASSEL), the Universal Network Enabled Analysis Kit (UNEAK), and the haplotype-based FreeBayes pipeline (FreeBayes). The resulting numbers of SNPs, marker density, minor allele frequency (MAF) and heterozygosity were compared among the pipelines. The TASSEL pipeline generated more markers with the highest density and MAF, whereas the highest heterozygosity was obtained by the UNEAK pipeline. The FreeBayes pipeline generated tetraploid genotypes, with the least number of markers. SNP markers generated from each pipeline were used independently for marker-trait association. Markers significantly associated with VW resistance identified by each pipeline were compared. Similar marker loci were found on chromosomes 5, 6, and 7, whereas different loci on chromosome 1, 2, 3, and 4 were identified by different pipelines. Most significant markers were located on chromosome 6 and they were identified by all three pipelines. Of those identified, several loci were linked to known genes whose functions are involved in the plants’ resistance to pathogens. Further investigation on these loci and their linked genes would provide insight into understanding molecular mechanisms of VW resistance in alfalfa. Functional markers closely linked to the resistance loci would be useful for MAS to improve alfalfa cultivars with enhanced resistance to the disease.

Genome-wide haplotype-based association analysis of key traits of plant lodging and architecture of maize identifies major determinants for leaf angle: hapLA4

“…The linear model used in the GWAS considered the matrix information of Q (genetic structure) and K (kinship), to reduce the false positive rate and correct the spurious associations in the analysis [35]. According to the analysis of mixed models, a total of 163 SNPs were identified to be associated with the four traits of interest, 15 of which were associated in both growing seasons and 11 of which were shared among multiple traits.…”

“…The readings with a quality score below 80 on the Phred scale (genotyping quality) [34] were eliminated. Finally, the database was filtered considering an MAF > 0.01 and a proportion of missing data per site < 90% [35].…”

Genetic Loci Associated with Salt Tolerance in Advanced Breeding Populations of Tetraploid Alfalfa Using Genome‐Wide Association Studies

“…(Bonhomme et al, 2014). In alfalfa, GWAS have been conducted to identify SNP loci for forage quality (Biazzi et al, 2017; Jia et al, 2017), drought (Zhang et al, 2015; Yu, 2017), and salt tolerance (Yu et al, 2016; Liu and Yu, 2017), fall dormancy (Munjal et al, 2017), and disease resistance (Yu et al, 2017). These genome‐wide SNP markers provide a platform for fine mapping of QTL at the whole‐genome level toward MAS and genomic selection (GS) (Li and Brummer 2012).…”

The History of Lentil (Lens culinaris subsp. culinaris) Domestication and Spread as Revealed by Genotyping-by-Sequencing of Wild and Landrace Accessions

“…Using a cluster reference method, instead of a reference genome, we were still able to generate sound phylogenetic inferences from GBS data ( Figure 1 ). The use of different pipelines and filtering criteria on GBS data is known to affect the number and quality of SNPs identified ( Torkamaneh et al, 2016 ; Yu et al, 2017 ; Loureiro et al, 2020b ). Our filtering conditions produced widely different numbers of SNPs requiring decisions to be made between loose or more restrictive filtering conditions (more coverage, less missing data, higher MAFs) for different types of analyses.…”