Abstract
A multitude of model and non-model species studies have now taken full advantage of powerful high-throughput genotyping advances such as SNP arrays and genotyping-by-sequencing (GBS) technology to investigate the genetic basis of trait variation. However, due to incomplete genome coverage by these technologies, the identified SNPs are likely in linkage disequilibrium (LD) with the causal polymorphisms, rather than be causal themselves. In addition, researchers could benefit from annotations for the identified candidate SNPs and, simultaneously, for all neighboring genes in genetic linkage. In such case, LD extent estimation surrounding the candidate SNPs is required to determine the regions encompassing genes of interest. We describe here an automated pipeline, “LD-annot,” designed to delineate specific regions of interest for a given experiment and candidate polymorphisms on the basis of LD extent, and furthermore, provide annotations for all genes within such regions. LD-annot uses standard file formats, bioinformatics tools, and languages to provide identifiers, coordinates, and annotations for genes in genetic linkage with each candidate polymorphism. Although the focus lies upon SNP arrays and GBS data as they are being routinely deployed, this pipeline can be applied to a variety of datasets as long as genotypic data are available for a high number of polymorphisms and formatted into a vcf file. A checkpoint procedure in the pipeline allows to test several threshold values for linkage without having to rerun the entire pipeline, thus saving the user computational time and resources. We applied this new pipeline to four different sample sets: two breeding populations GBS datasets, one within-pedigree SNP set coming from whole genome sequencing (WGS), and a very large multi-varieties SNP dataset obtained from WGS, representing variable sample sizes, and numbers of polymorphisms. LD-annot performed within minutes, even when very high numbers of polymorphisms are investigated and thus will efficiently assist research efforts aimed at identifying biologically meaningful genetic polymorphisms underlying phenotypic variation. LD-annot tool is available under a GPL license from https://github.com/ArnaudDroitLab/LD-annot.
Highlights
The progress in molecular technologies enabled the study of genetic variants at the genome level, in both model and nonmodel species, such as Genome-Wide Association Studies (GWAS) identifying genetic variants likely involved in variation of interesting quantitative traits or in adaptation to environmental stress
We assessed the performance of our tool through the analysis of the four datasets previously described and covering a large distribution in numbers of genotyped and candidate SNPs, and a variety of r2 thresholds
As datasets are always increasing in size with technological progress and the usual need to test several r2 thresholds, we believe the checkpoint procedure will be beneficial to the genomics research community
Summary
The progress in molecular technologies enabled the study of genetic variants at the genome level, in both model and nonmodel species, such as Genome-Wide Association Studies (GWAS) identifying genetic variants likely involved in variation of interesting quantitative traits or in adaptation to environmental stress. Research projects based on either of these variant detection approaches often investigate the genomic basis of trait variations related to agronomic performance in cultivated plants or animals (Carter et al, 2018; Torkamaneh et al, 2018;), the dispersion of invasive species (White et al, 2013; Roe et al, 2018), or species’ adaptation (Hess et al, 2012; Keller et al, 2013), for instance Such studies typically use regression models to select candidate SNPs presenting significant trait variations between distinct genotypic classes. These polymorphisms might not be directly responsible for phenotypic variations but in linkage disequilibrium (LD) with larger genomic regions encompassing untested genetic variants that might be truly causal for the studied phenotypic variation
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