Utilizing Genotype Imputation for the Augmentation of Sequence Data

Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
PLoS ONE (Impact Factor: 3.23). 06/2010; 5(6):e11018. DOI: 10.1371/journal.pone.0011018
Source: PubMed


In recent years, capabilities for genotyping large sets of single nucleotide polymorphisms (SNPs) has increased considerably with the ability to genotype over 1 million SNP markers across the genome. This advancement in technology has led to an increase in the number of genome-wide association studies (GWAS) for various complex traits. These GWAS have resulted in the implication of over 1500 SNPs associated with disease traits. However, the SNPs identified from these GWAS are not necessarily the functional variants. Therefore, the next phase in GWAS will involve the refining of these putative loci.
A next step for GWAS would be to catalog all variants, especially rarer variants, within the detected loci, followed by the association analysis of the detected variants with the disease trait. However, sequencing a locus in a large number of subjects is still relatively expensive. A more cost effective approach would be to sequence a portion of the individuals, followed by the application of genotype imputation methods for imputing markers in the remaining individuals. A potentially attractive alternative option would be to impute based on the 1000 Genomes Project; however, this has the drawbacks of using a reference population that does not necessarily match the disease status and LD pattern of the study population. We explored a variety of approaches for carrying out the imputation using a reference panel consisting of sequence data for a fraction of the study participants using data from both a candidate gene sequencing study and the 1000 Genomes Project.
Imputation of genetic variation based on a proportion of sequenced samples is feasible. Our results indicate the following sequencing study design guidelines which take advantage of the recent advances in genotype imputation methodology: Select the largest and most diverse reference panel for sequencing and genotype as many "anchor" markers as possible.

8 Reads
  • Source
    • "In addition to imputation of common variants, which are generally used in genome-wide association studies, there is increasing interest in imputation of rare variants from sequencing data. Fridley et al. have explored cost-effective ways to impute rare variants and suggest using sequence data from the 1000 genomes project and possibly combining this information with actual sequence data from a subset of the population being studied [19]. This approach might work in Pima Indians. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Background Genotype imputation is commonly used in genetic association studies to test untyped variants using information on linkage disequilibrium (LD) with typed markers. Imputing genotypes requires a suitable reference population in which the LD pattern is known, most often one selected from HapMap. However, some populations, such as American Indians, are not represented in HapMap. In the present study, we assessed accuracy of imputation using HapMap reference populations in a genome-wide association study in Pima Indians. Results Data from six randomly selected chromosomes were used. Genotypes in the study population were masked (either 1% or 20% of SNPs available for a given chromosome). The masked genotypes were then imputed using the software Markov Chain Haplotyping Algorithm. Using four HapMap reference populations, average genotype error rates ranged from 7.86% for Mexican Americans to 22.30% for Yoruba. In contrast, use of the original Pima Indian data as a reference resulted in an average error rate of 1.73%. Conclusions Our results suggest that the use of HapMap reference populations results in substantial inaccuracy in the imputation of genotypes in American Indians. A possible solution would be to densely genotype or sequence a reference American Indian population.
    PLoS ONE 07/2014; 9(7):e102544. DOI:10.1371/journal.pone.0102544 · 3.23 Impact Factor
  • Source
    • "It also enables novel variants distinctive to the study sample to be imputed. Employing sequences from a candidate gene and the 1000 Genomes Project, Fridley et al. (2010) demonstrated the feasibility of imputing genetic variants based on a sequenced proportion of a study sample, and they suggested sequencing “the largest and most diverse” subset. In a theoretical study, Jewett et al. (2012) found that including sequenced haplotypes from the study population in the reference panel improved imputation accuracy, even if the external panel was taken from a closely related population. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The recent dramatic cost reduction of next-generation sequencing technology enables investigators to assess most variants in the human genome to identify risk variants for complex diseases. However, sequencing large samples remains very expensive. For a study sample with existing genotype data, such as array data from genome-wide association studies, a cost-effective approach is to sequence a subset of the study sample, and then to impute the rest of the study sample using the sequenced subset as a reference panel. The use of such an internal reference panel identifies population-specific variants and avoids the problem of a substantial mismatch in ancestry background between the study population and the reference population. To efficiently select an internal panel, we introduce an idea of phylogenetic diversity from mathematical phylogenetics and comparative genomics. We propose the "most diverse reference panel," defined as the subset with the maximal "phylogenetic diversity," thereby incorporating individuals that span a diverse range of genotypes within the sample. Using data both from simulations and from the 1000 Genomes Project, we show that the most diverse reference panel can considerably improve the imputation accuracy compared to randomly selected reference panels, especially for the imputation of rare variants. The improvement in imputation accuracy holds across different maker densities, reference panel sizes, and lengths for the imputed segments. We thus propose a novel strategy for planning sequencing studies on samples with existing genotype data.
    Genetics 08/2013; 195(2). DOI:10.1534/genetics.113.154591 · 5.96 Impact Factor
  • Source
    • "To date, most of genotype imputation evaluations were done in samples of European, African, and Asian ancestry (Pei et al., 2008; Huang et al., 2009, 2011; Fridley et al., 2010; Shriner et al., 2010; Howie et al., 2011; Li et al., 2011) and only limited reports explored the imputation using 1KGP data (Sung et al., 2011). We present the first extensive evaluation of genotyping imputation for Latinos using the HapMap and 1KGP reference panels. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Genotype imputation is a vital tool in genome-wide association studies (GWAS) and meta-analyses of multiple GWAS results. Imputation enables researchers to increase genomic coverage and to pool data generated using different genotyping platforms. HapMap samples are often employed as the reference panel. More recently, the 1000 Genomes Project resource is becoming the primary source for reference panels. Multiple GWAS and meta-analyses are targeting Latinos, the most populous, and fastest growing minority group in the US. However, genotype imputation resources for Latinos are rather limited compared to individuals of European ancestry at present, largely because of the lack of good reference data. One choice of reference panel for Latinos is one derived from the population of Mexican individuals in Los Angeles contained in the HapMap Phase 3 project and the 1000 Genomes Project. However, a detailed evaluation of the quality of the imputed genotypes derived from the public reference panels has not yet been reported. Using simulation studies, the Illumina OmniExpress GWAS data from the Los Angles Latino Eye Study and the MACH software package, we evaluated the accuracy of genotype imputation in Latinos. Our results show that the 1000 Genomes Project AMR + CEU + YRI reference panel provides the highest imputation accuracy for Latinos, and that also including Asian samples in the panel can reduce imputation accuracy. We also provide the imputation accuracy for each autosomal chromosome using the 1000 Genomes Project panel for Latinos. Our results serve as a guide to future imputation based analysis in Latinos.
    Frontiers in Genetics 06/2012; 3:117. DOI:10.3389/fgene.2012.00117
Show more


8 Reads
Available from