LD-annot: A Bioinformatics Tool to Automatically Provide Candidate SNPs With Annotations for Genetically Linked Genes

Area of Science:

• Genetics and Genomics
• Bioinformatics
• Population Genetics

Background:

• High-throughput genotyping technologies like SNP arrays and genotyping-by-sequencing (GBS) are widely used to study the genetic basis of trait variation.
• Identified single nucleotide polymorphisms (SNPs) are often in linkage disequilibrium (LD) with causal variants due to incomplete genome coverage.
• Accurate annotation of candidate SNPs and neighboring genes is crucial for understanding genetic linkage and trait heritability.

Purpose of the Study:

• To develop an automated bioinformatics pipeline, LD-annot, for delineating regions of interest based on linkage disequilibrium (LD) extent.
• To provide comprehensive annotations for genes located within these LD regions surrounding candidate polymorphisms.
• To facilitate the identification of biologically meaningful genetic polymorphisms underlying phenotypic variation.

Main Methods:

• The LD-annot pipeline utilizes standard file formats and bioinformatics tools to process genotypic data (e.g., VCF files).
• It estimates LD extent around candidate SNPs to define specific regions of interest.
• A checkpoint procedure allows for testing multiple LD threshold values without rerunning the entire analysis, optimizing computational resources.

Main Results:

• The LD-annot pipeline successfully delineated regions of interest and annotated linked genes across diverse datasets, including GBS and whole genome sequencing (WGS) SNP data.
• The tool demonstrated high efficiency, performing analyses within minutes even with large datasets and numerous polymorphisms.
• It provides identifiers, coordinates, and annotations for genes in genetic linkage with candidate polymorphisms.

Conclusions:

• LD-annot is an efficient and automated solution for identifying and annotating genes associated with trait variation using SNP and GBS data.
• The pipeline significantly assists researchers in pinpointing causative genetic variants and understanding their genomic context.
• Its applicability to various genotypic datasets and its user-friendly design make it a valuable tool for genetic research.