Dataset Information

Evaluation and application of summary statistic imputation to discover new height-associated loci.

ABSTRACT: As most of the heritability of complex traits is attributed to common and low frequency genetic variants, imputing them by combining genotyping chips and large sequenced reference panels is the most cost-effective approach to discover the genetic basis of these traits. Association summary statistics from genome-wide meta-analyses are available for hundreds of traits. Updating these to ever-increasing reference panels is very cumbersome as it requires reimputation of the genetic data, rerunning the association scan, and meta-analysing the results. A much more efficient method is to directly impute the summary statistics, termed as summary statistics imputation, which we improved to accommodate variable sample size across SNVs. Its performance relative to genotype imputation and practical utility has not yet been fully investigated. To this end, we compared the two approaches on real (genotyped and imputed) data from 120K samples from the UK Biobank and show that, genotype imputation boasts a 3- to 5-fold lower root-mean-square error, and better distinguishes true associations from null ones: We observed the largest differences in power for variants with low minor allele frequency and low imputation quality. For fixed false positive rates of 0.001, 0.01, 0.05, using summary statistics imputation yielded a decrease in statistical power by 9, 43 and 35%, respectively. To test its capacity to discover novel associations, we applied summary statistics imputation to the GIANT height meta-analysis summary statistics covering HapMap variants, and identified 34 novel loci, 19 of which replicated using data in the UK Biobank. Additionally, we successfully replicated 55 out of the 111 variants published in an exome chip study. Our study demonstrates that summary statistics imputation is a very efficient and cost-effective way to identify and fine-map trait-associated loci. Moreover, the ability to impute summary statistics is important for follow-up analyses, such as Mendelian randomisation or LD-score regression.

SUBMITTER: Rueger S

PROVIDER: S-EPMC5983877 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evaluation and application of summary statistic imputation to discover new height-associated loci.

Rüeger Sina S McDaid Aaron A Kutalik Zoltán Z

PLoS genetics 20180521 5

As most of the heritability of complex traits is attributed to common and low frequency genetic variants, imputing them by combining genotyping chips and large sequenced reference panels is the most cost-effective approach to discover the genetic basis of these traits. Association summary statistics from genome-wide meta-analyses are available for hundreds of traits. Updating these to ever-increasing reference panels is very cumbersome as it requires reimputation of the genetic data, rerunning t ...[more]

PMID: 29782485

Similar Datasets

Project description:BackgroundAs the amount of data from genome wide association studies grows dramatically, many interesting scientific questions require imputation to combine or expand datasets. However, there are two situations for which imputation has been problematic: (1) polymorphisms with low minor allele frequency (MAF), and (2) datasets where subjects are genotyped on different platforms. Traditional measures of imputation cannot effectively address these problems.Methodology/principal findingsWe introduce a new statistic, the imputation quality score (IQS). In order to differentiate between well-imputed and poorly-imputed single nucleotide polymorphisms (SNPs), IQS adjusts the concordance between imputed and genotyped SNPs for chance. We first evaluated IQS in relation to minor allele frequency. Using a sample of subjects genotyped on the Illumina 1 M array, we extracted those SNPs that were also on the Illumina 550 K array and imputed them to the full set of the 1 M SNPs. As expected, the average IQS value drops dramatically with a decrease in minor allele frequency, indicating that IQS appropriately adjusts for minor allele frequency. We then evaluated whether IQS can filter poorly-imputed SNPs in situations where cases and controls are genotyped on different platforms. Randomly dividing the data into "cases" and "controls", we extracted the Illumina 550 K SNPs from the cases and imputed the remaining Illumina 1 M SNPs. The initial Q-Q plot for the test of association between cases and controls was grossly distorted (lambda = 1.15) and had 4016 false positives, reflecting imputation error. After filtering out SNPs with IQS<0.9, the Q-Q plot was acceptable and there were no longer false positives. We then evaluated the robustness of IQS computed independently on the two halves of the data. In both European Americans and African Americans the correlation was >0.99 demonstrating that a database of IQS values from common imputations could be used as an effective filter to combine data genotyped on different platforms.Conclusions/significanceIQS effectively differentiates well-imputed and poorly-imputed SNPs. It is particularly useful for SNPs with low minor allele frequency and when datasets are genotyped on different platforms.

Project description:Adult body height is a quantitative trait for which genome-wide association studies (GWAS) have identified numerous loci, primarily in European populations. These loci, comprising common variants, explain <10% of the phenotypic variance in height. We searched for novel associations between height and common (minor allele frequency, MAF ≥5%) or infrequent (0.5% < MAF < 5%) variants across the exome in African Americans. Using a reference panel of 1692 African Americans and 471 Europeans from the National Heart, Lung, and Blood Institute's (NHLBI) Exome Sequencing Project (ESP), we imputed whole-exome sequence data into 13 719 African Americans with existing array-based GWAS data (discovery). Variants achieving a height-association threshold of P < 5E-06 in the imputed dataset were followed up in an independent sample of 1989 African Americans with whole-exome sequence data (replication). We used P < 2.5E-07 (=0.05/196 779 variants) to define statistically significant associations in meta-analyses combining the discovery and replication sets (N = 15 708). We discovered and replicated three independent loci for association: 5p13.3/C5orf22/rs17410035 (MAF = 0.10, β = 0.64 cm, P = 8.3E-08), 13q14.2/SPRYD7/rs114089985 (MAF = 0.03, β = 1.46 cm, P = 4.8E-10) and 17q23.3/GH2/rs2006123 (MAF = 0.30; β = 0.47 cm; P = 4.7E-09). Conditional analyses suggested 5p13.3 (C5orf22/rs17410035) and 13q14.2 (SPRYD7/rs114089985) may harbor novel height alleles independent of previous GWAS-identified variants (r(2) with GWAS loci <0.01); whereas 17q23.3/GH2/rs2006123 was correlated with GWAS-identified variants in European and African populations. Notably, 13q14.2/rs114089985 is infrequent in African Americans (MAF = 3%), extremely rare in European Americans (MAF = 0.03%), and monomorphic in Asian populations, suggesting it may be an African-American-specific height allele. Our findings demonstrate that whole-exome imputation of sequence variants can identify low-frequency variants and discover novel variants in non-European populations.

Dataset Information

Evaluation and application of summary statistic imputation to discover new height-associated loci.

Publications

Evaluation and application of summary statistic imputation to discover new height-associated loci.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets