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VAR-seq (SNP-Seq) Analysis

How VAR-seq data analysis works

Detection of single nucleotide polymorphisms (SNPs) is an important step in understanding the relationship between a genotype and phenotype. A likely workflow in genetic variation studies is the analysis and identification of variants associated with a specific trait or population. Input data could be from a whole genome or whole exome sequencing. SNPs are useful because they provide information about polymorphism within a population, genetic changes influencing common disease, and drug efficacy.


SNP-seq data analysis includes but is not limited to:
  • Data Management/Quality Control and Trimming
  • Alignment to Reference Genome
  • Variant Detection
  • Variant Filtering
  • Data Visualization
Figure #1: An illustration of a Manhattan plot depicting several strongly associated risk loci. Credit: M. Kamran Ikram et al, Creative Commons Attribution 2.5 Generic License

B. Additional Analysis for GWAS-like Data

Sample QC Task Checking
  • Discordant sex information
  • Calculating missingness
  • Heterozygosity scores
  • Relatedness
Batch Reports
  • Remove duplicates
  • Minor allele frequencies
  • SNP missingness
  • Differential missingness
  • Hardy-Weinberg equilibrium deviations
Basic PLINK association tests, producing manhattan and Q plots
  • CMH association test - association analysis, account for clusters
  • Permutation testing
  • Logistic regression