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Performance of genotype imputation for rare variants identified in exons and flanking regions of genes.

Li Li | Yun Li | Sharon R Browning | Brian L Browning | Andrew J Slater | Xiangyang Kong | Jennifer L Aponte | Vincent E Mooser | Stephanie L Chissoe | John C Whittaker | Matthew R Nelson | Margaret Gelder Ehm
PloS one | 2011

Genotype imputation has the potential to assess human genetic variation at a lower cost than assaying the variants using laboratory techniques. The performance of imputation for rare variants has not been comprehensively studied. We utilized 8865 human samples with high depth resequencing data for the exons and flanking regions of 202 genes and Genome-Wide Association Study (GWAS) data to characterize the performance of genotype imputation for rare variants. We evaluated reference sets ranging from 100 to 3713 subjects for imputing into samples typed for the Affymetrix (500K and 6.0) and Illumina 550K GWAS panels. The proportion of variants that could be well imputed (true r(2)>0.7) with a reference panel of 3713 individuals was: 31% (Illumina 550K) or 25% (Affymetrix 500K) with MAF (Minor Allele Frequency) less than or equal 0.001, 48% or 35% with 0.0010.05. The performance for common SNPs (MAF>0.05) within exons and flanking regions is comparable to imputation of more uniformly distributed SNPs. The performance for rare SNPs (0.01

Pubmed ID: 21949800

Research resources used in this publication

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Additional research tools detected in this publication

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Associated grants

  • Agency: NHGRI NIH HHS, United States
    Id: R01 HG004960
  • Agency: NHGRI NIH HHS, United States
    Id: R01 HG005701
  • Agency: NHGRI NIH HHS, United States
    Id: R01HG005701
  • Agency: NHGRI NIH HHS, United States
    Id: R01HG004960

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This is a list of tools and resources that we have found mentioned in this publication.


MINIMAC (tool)

RRID:SCR_009292

Software application that is a low memory, computationally efficient implementation of the MaCH algorithm for genotype imputation. It is designed to work on phased genotypes and can handle very large reference panels with hundreds or thousands of haplotypes. The name has two parts. The first, mini, refers to the modest amount of computational resources it requires. The second, mac, is short hand for MaCH, our widely used algorithm for genotype imputation. (entry from Genetic Analysis Software)

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BEAGLE (tool)

RRID:SCR_001789

Software package for analysis of large-scale genetic data sets with hundreds of thousands of markers genotyped on thousands of samples. BEAGLE can * phase genotype data (i.e. infer haplotypes) for unrelated individuals, parent-offspring pairs, and parent-offspring trios. * infer sporadic missing genotype data. * impute ungenotyped markers that have been genotyped in a reference panel. * perform single marker and haplotypic association analysis. * detect genetic regions that are homozygous-by-descent in an individual or identical-by-descent in pairs of individuals. Beagle can also be used in conjunction with PRESTO, a program for fast and flexible permutation testing. PRESTO can compute empirical distributions of order statistics, analyze stratified data, and determine significance levels for one-stage and two-stage genetic association studies. BEAGLE is written in Java and runs on any computing platform with a Java version 1.6 interpreter (e.g. Windows, Unix, Linux, Solaris, Mac).

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