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Notes from Illumina webinar: GenomeStudio Genotyping: Creating Custom Cluster Files for Infinium Arrays.

Background and raw genotype data:

  • Scanner detects red and green fluorescent intenseities for each locaus (A and B) to produce genotyping data (intensity data files - idats).
  • GenomeStudio converts A and B color channel signals into genoype calls.
  • A and B intensity plots (A vs. B) are transformed into polar coordinates plots (theta vs. R, where theta is the angle from the line Y=0 [x-axis] to a given point i (A,B) and R = sum of A and B signal intensities).
  • GS uses algorithms to create clusters and assign genotypes.
  • Clustering algorithm GenTrain surveys data to determine shape of genotype cluster (3 clusters expected), algorithm works in 3 steps (SNP specific):
    • Preliminary clustering based on samples, looks for groups.
    • Assigns 3 clusters (AA/AB/BB).
    • Scores them based on tighteness and spread between them and HW, perfect GenTrain score = 1.
  • Calling algorithm GenCall determines which genotype bin each data point belongs to after clustered are defined.
    • GenCall score is SNP and sample specific, measures how well a smaple fits into a given cluster.
    • SNP with poor GenTrain score then this SNP will also have low GenCall scores.
    • Threshold for calling genotypes is recommended at 0.15.

Cluster files:

  • Matrices, with one row per SNP with the following columns:
    • Cluster positions: mean and SD of R and Theta for each AA, AB, BB cluster in normalized coordinates for every SNP.
    • Cluster score information, i.e. GenTrain score.
    • Cluster seperation.
  • Cluster file (.egt) and manifest file (.bpm) are needed to define genotypes from intensity files (.idats).
  • Standard cluster files are provided with each version of an array.
  • Reclustering some or all SNPs will make your calls more accurate (since they are based on your data not a reference panel).

Designing a custom cluster file:

  • Sample choice:
    • Use normal samples (i.e. controls)
    • Samples of comparable, good quality
    • >100 individauls
  • Conditions:
    • >3 runs
    • >3 reagent lots
    • Representative number of operators/technicians/robtots

Creating a cluster file:

  1. Cluster:
    1. Cluster all SNPs (takes a long time)
    2. Evaluate samples and remove outliers
  2. Recluster:
    1. Cluster sex chromosomes
    2. Cluster autosomes
  3. Review and edit:
    1. Use filters and score to evaluate SNPs
    2. Correct or zero SNPs as needed

See technical note with additional details.