A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data.

Young Jin Kim,Bong-Jo Kim,Taesung Park,Juyoung Lee

doi:10.1186/s12864-015-2192-y

Young Jin Kim, Bong-Jo Kim + Show 2 more

Open Access

https://doi.org/10.1186/s12864-015-2192-y

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Abstract

BackgroundRare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants.ResultsIn this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data.ConclusionsOur study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants.

Highlights

Rare variants have gathered increasing attention as a possible alternative source of missing heritability
We propose a new strategy to increase the accuracy of imputation of rare variants by improving a new genotype panel by combining exome chip with existing single nucleotide polymorphism (SNP) chip data
We show that the new genotype panel of combined data of exome chip and SNP chip improves imputation quality of imputed rare variants

Summary

Introduction

Rare variants have gathered increasing attention as a possible alternative source of missing heritability. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. Recent large scale sequencing studies reported that the population frequencies of a large proportion of discovered variants were rare (Minor Allele Frequency (MAF) < 1 %) [6,7,8]. Given their abundance, rare variants have been increasingly recognized as an alternative source of missing heritability [5, 7, 9]. Large-scale, population-based genomic sequencing studies are not yet feasible, due to high cost and computationintensive analysis [10, 11]

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