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Genotypic discrepancies arising from imputation.

Anthony L Hinrichs1, Robert C Culverhouse2, Brian K Suarez3

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|December 19, 2014
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Summary
This summary is machine-generated.

Combining genome-wide association study (GWAS) chip data with whole genome sequencing improves family genetic analysis. Imputation accuracy is high, but challenges arise when inferring missing sequence data, potentially impacting genetic studies.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Whole genome sequencing (WGS) of all family members is ideal for genetic analysis.
  • Combining WGS data from key individuals with genome-wide association study (GWAS) chip genotypes offers a cost-effective alternative.
  • This hybrid strategy was employed by Genetic Analysis Workshop 18 (GAW18) data providers.

Purpose of the Study:

  • To evaluate the imputation quality of the combined WGS and GWAS strategy.
  • To identify potential consequences of imputation errors in family genetic analysis.
  • To assess the accuracy of inferring sequence-level genotypes across families.

Main Methods:

  • Comparison of genotype calls between GWAS chips and imputed sequence data for the same variants.
  • Analysis of discrepancies to assess imputation accuracy.
  • Examination of imputation performance in sequenced and unsequenced individuals within and across pedigrees.

Main Results:

  • The inference and imputation process generally performed well.
  • Discrepancies increased when imputing missing data in already sequenced individuals.
  • Higher discrepancy rates were observed when inferring genotypes for unsequenced individuals using data from other pedigrees.

Conclusions:

  • The imputation strategy is largely effective but has limitations.
  • Inferring missing sequence data, especially in sequenced individuals, may introduce errors.
  • Incorrect imputation, particularly in founders, can lead to false positives and reduced power in family-based transmission tests.