Imputation-based genomic coverage assessments of current human genotyping arrays - PubMed (original) (raw)

Imputation-based genomic coverage assessments of current human genotyping arrays

Sarah C Nelson et al. G3 (Bethesda). 2013.

Abstract

Microarray single-nucleotide polymorphism genotyping, combined with imputation of untyped variants, has been widely adopted as an efficient means to interrogate variation across the human genome. "Genomic coverage" is the total proportion of genomic variation captured by an array, either by direct observation or through an indirect means such as linkage disequilibrium or imputation. We have performed imputation-based genomic coverage assessments of eight current genotyping arrays that assay from ~0.3 to ~5 million variants. Coverage was determined separately in each of the four continental ancestry groups in the 1000 Genomes Project phase 1 release. We used the subset of 1000 Genomes variants present on each array to impute the remaining variants and assessed coverage based on correlation between imputed and observed allelic dosages. More than 75% of common variants (minor allele frequency > 0.05) are covered by all arrays in all groups except for African ancestry, and up to ~90% in all ancestries for the highest density arrays. In contrast, less than 40% of less common variants (0.01 < minor allele frequency < 0.05) are covered by low density arrays in all ancestries and 50-80% in high density arrays, depending on ancestry. We also calculated genome-wide power to detect variant-trait association in a case-control design, across varying sample sizes, effect sizes, and minor allele frequency ranges, and compare these array-based power estimates with a hypothetical array that would type all variants in 1000 Genomes. These imputation-based genomic coverage and power analyses are intended as a practical guide to researchers planning genetic studies.

Keywords: SNP microarrays; genome-wide association study; genomic coverage; power.

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Figures

Figure 1

Figure 1

Study design. Schematic of the method used to assess genomic coverage of each array. Throughout the diagram, bold solid lines around boxes indicate observed genotypes (i.e., variant calls from the 1000 Genomes Project phase 1 integrated release, version 3), whereas dashed lines indicate imputed genotypes.

Figure 2

Figure 2

Fraction of variants passing an imputation r2 threshold of 0.8, by MAF bin and ancestry group. The imputation r2 metric plotted here is the squared correlation between imputed and observed allelic dosage in the samples comprising the ancestry group. The y-axis is the proportion of variants (imputed and observed) with imputation r2 ≥ 0.8, restricted to variants with at least two copies of the minor allele in the given ancestry group. The x-axis position of each array corresponds to the number of unique positions assayed by that array (see Table 2, column 5). Thus. the order of the arrays on each axis is as follows: HumanCore, HumanCore+Exome, Axiom Biobank, OmniExpress, Axiom World Array 4, Omni2.5M, Omni2.5M+Exome, and Omni5M. Panel (A) is for variants with at least two copies of the minor allele and MAF ≤ 0.01, (B) for 0.01 < MAF ≤ 0.05, (C) for MAF > 0.01, and (D) for MAF > 0.05.

Figure 3

Figure 3

Mean MA concordance, by MAF bin and ancestry group. The y-axis values are mean MA concordance in samples comprising the given ancestry group. MA concordance is defined as the concordance (percent agreement) between observed and most likely imputed genotype, when at least one of those two genotypes contains one or two copies of the minor allele. Variants were restricted to those with at least two copies of the minor allele in the given ancestry group. The x-axis position of each array corresponds to the number of unique positions assayed by that array (see Table 2, column 5). Thus the order of the arrays on each axis is as follows: HumanCore, HumanCore+Exome, Axiom Biobank, OmniExpress, Axiom World Array 4, Omni2.5M, Omni2.5M+Exome, and Omni5M. (A) Variants with at least two copies of the minor allele and MAF ≤ 0.01, (B) for 0.01 < MAF ≤ 0.05, (C) for MAF > 0.01, and (D) for MAF > 0.05.

Figure 4

Figure 4

Genome-wide power estimates for GRR values of 1.2, 1.3, and 1.4, for common autosomal variants (MAF > 0.05). The array Omni2.5+Exome is not shown in these plots because it is indistinguishable at this resolution from the Omni2.5M array. In the legend, “1000 Genomes” refers to a hypothetical array in which all variants in the 1000 Genomes dataset would be typed.

Figure 5

Figure 5

Genome-wide power estimates for GRR values of 1.2, 1.3, and 1.4, for less common autosomal variants (0.01 < MAF ≤ 0.05). The array Omni2.5+Exome is not shown in these plots because it is indistinguishable at this resolution from the Omni2.5M array. In the legend, “1000 Genomes” refers to a hypothetical array in which all variants in the 1000 Genomes dataset would be typed.

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References

    1. Abecasis G. R., Auton A., Brooks L. D., DePristo M. A., Durbin R. M., et al. , 2012. An integrated map of genetic variation from 1,092 human genomes. Nature 491: 56–65 - PMC - PubMed
    1. Affymetrix Inc. 2012 Affymetrix introduces axiom biobank arrays for genotyping studies. Available at: http://investor.affymetrix.com/phoenix.zhtml?c=116408&p=irol-newsArticle... Accessed April 30, 2013.
    1. Barrett J. C., Cardon L. R., 2006. Evaluating coverage of genome-wide association studies. Nat. Genet. 38: 659–662 - PubMed
    1. Browning S. R., Browning B. L., 2011. Haplotype phasing: existing methods and new developments. Nat. Rev. Genet. 12: 703–714 - PMC - PubMed
    1. Carlson C. S., Eberle M. A., Rieder M. J., Yi Q., Kruglyak L., et al. , 2004. Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. Am. J. Hum. Genet. 74: 106–120 - PMC - PubMed

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