45 research outputs found

    Performance of Genotype Imputation for Low Frequency and Rare Variants from the 1000 Genomes

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    <div><p>Genotype imputation is now routinely applied in genome-wide association studies (GWAS) and meta-analyses. However, most of the imputations have been run using HapMap samples as reference, imputation of low frequency and rare variants (minor allele frequency (MAF) < 5%) are not systemically assessed. With the emergence of next-generation sequencing, large reference panels (such as the 1000 Genomes panel) are available to facilitate imputation of these variants. Therefore, in order to estimate the performance of low frequency and rare variants imputation, we imputed 153 individuals, each of whom had 3 different genotype array data including 317k, 610k and 1 million SNPs, to three different reference panels: the 1000 Genomes pilot March 2010 release (1KGpilot), the 1000 Genomes interim August 2010 release (1KGinterim), and the 1000 Genomes phase1 November 2010 and May 2011 release (1KGphase1) by using IMPUTE version 2. The differences between these three releases of the 1000 Genomes data are the sample size, ancestry diversity, number of variants and their frequency spectrum. We found that both reference panel and GWAS chip density affect the imputation of low frequency and rare variants. 1KGphase1 outperformed the other 2 panels, at higher concordance rate, higher proportion of well-imputed variants (info>0.4) and higher mean info score in each MAF bin. Similarly, 1M chip array outperformed 610K and 317K. However for very rare variants (MAF≤0.3%), only 0–1% of the variants were well imputed. We conclude that the imputation of low frequency and rare variants improves with larger reference panels and higher density of genome-wide genotyping arrays. Yet, despite a large reference panel size and dense genotyping density, very rare variants remain difficult to impute.</p></div

    Overview of the imputation performances for the 3 genome-wide genotype arrays based on different reference panels.

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    <p>* SNP QC was done</p><p>** Well-imputed SNPs were those with proper info ≥ 0.4</p><p>Overview of the imputation performances for the 3 genome-wide genotype arrays based on different reference panels.</p

    <i>m</i><sub><i>e</i></sub> estimation in UK Biobank and Chinese cohorts.

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    Explicitly sharing individual level data in genomics studies has many merits comparing to sharing summary statistics, including more strict QCs, common statistical analyses, relative identification and improved statistical power in GWAS, but it is hampered by privacy or ethical constraints. In this study, we developed encG-reg, a regression approach that can detect relatives of various degrees based on encrypted genomic data, which is immune of ethical constraints. The encryption properties of encG-reg are based on the random matrix theory by masking the original genotypic matrix without sacrificing precision of individual-level genotype data. We established a connection between the dimension of a random matrix, which masked genotype matrices, and the required precision of a study for encrypted genotype data. encG-reg has false positive and false negative rates equivalent to sharing original individual level data, and is computationally efficient when searching relatives. We split the UK Biobank into their respective centers, and then encrypted the genotype data. We observed that the relatives estimated using encG-reg was equivalently accurate with the estimation by KING, which is a widely used software but requires original genotype data. In a more complex application, we launched a finely devised multi-center collaboration across 5 research institutes in China, covering 9 cohorts of 54,092 GWAS samples. encG-reg again identified true relatives existing across the cohorts with even different ethnic backgrounds and genotypic qualities. Our study clearly demonstrates that encrypted genomic data can be used for data sharing without loss of information or data sharing barrier.</div

    Workflow of <i>encG-reg</i> and its practical timeline as exercised in Chinese cohorts.

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    The mathematical details of encG-reg are simply algebraic, but its inter-cohort implementation involves coordination. (A) We illustrate its key steps, the time cost of which was adapted from the present exercise for 9 Chinese datasets (here simplified as three cohorts). Cohort assembly: It took us about a week to call and got positive responses from our collaborators (See Table 3), who agreed with our research plan. Inter-cohort QC: we received allele frequencies reports from each cohort and started to implement inter-cohort QC according to “geo-geno” analysis (see Fig 6). This step took about two weeks. Encrypt genotypes: upon the choice of the exercise, it could be exhaustive design (see UKB example), which may maximize the statistical power but with increased logistics such as generating pairwise Sij; in the Chinese cohorts study we used parsimony design, and generated a unique S given 500 SNPs that were chosen from the 7,009 common SNPs. It took about a week to determine the number of SNPs and the dimension of k according to Eq 3 and 4, and to evaluate the effective number of markers. Perform encG-reg and validation: we conducted inter-cohort encG-reg and validated the results (see Fig 7 and Table 4). It took one week. (B) Two interactions between data owners and central analyst, including example data for exchange and possible attacks and corresponding preventative strategies.</p
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