How Array Design creates SNP Ascertainment Bias
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ABSTRACT:
PROVIDER: PRJEB41928 | ENA |
REPOSITORIES: ENA
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Project description:Single nucleotide polymorphisms (SNPs), genotyped with arrays, have become a widely used marker type in population genetic analyses over the last 10 years. However, compared to whole genome re-sequencing data, arrays are known to lack a substantial proportion of globally rare variants and tend to be biased towards variants present in populations involved in the development process of the respective array. This affects population genetic estimators and is known as SNP ascertainment bias. We investigated factors contributing to ascertainment bias in array development by redesigning the Axiom™ Genome-Wide Chicken Array in silico and evaluating changes in allele frequency spectra and heterozygosity estimates in a stepwise manner. A sequential reduction of rare alleles during the development process was shown. This was mainly caused by the identification of SNPs in a limited set of populations and a within-population selection of common SNPs when aiming for equidistant spacing. These effects were shown to be less severe with a larger discovery panel. Additionally, a generally massive overestimation of expected heterozygosity for the ascertained SNP sets was shown. This overestimation was 24% higher for populations involved in the discovery process than not involved populations in case of the original array. The same was observed after the SNP discovery step in the redesign. However, an unequal contribution of populations during the SNP selection can mask this effect but also adds uncertainty. Finally, we make suggestions for the design of specialized arrays for large scale projects where whole genome re-sequencing techniques are still too expensive.
| S-EPMC8009414 | biostudies-literature
Project description:Spot intensity serves as a proxy for gene expression in dual-label microarray experiments. Dye bias is defined as an intensity difference between samples labeled with different dyes attributable to the dyes instead of the gene expression in the samples. Dye bias that is not removed by array normalization can introduce bias into comparisons between samples of interest. But if the bias is consistent across the samples for the same gene, it can be corrected by proper experimental design and analysis. If the dye bias is not consistent across samples for the same gene, but is different for different samples, then removing the bias becomes more problematic, perhaps indicating a technical limitation to the ability of fluorescent signals to accurately represent gene expression. Thus, it is important to characterize dye bias to determine: (1) whether it will be removed for all genes by array normalization, (2) whether it will not be removed by normalization but can be removed by proper experimental design and analysis and (3) whether dye bias correction is more problematic than either of these and is not easily removable. Keywords: dye swap design
2006-06-06 | GSE4982 | GEO
Project description:Spot intensity serves as a proxy for gene expression in dual-label microarray experiments. Dye bias is defined as an intensity difference between samples labeled with different dyes attributable to the dyes instead of the gene expression in the samples. Dye bias that is not removed by array normalization can introduce bias into comparisons between samples of interest. But if the bias is consistent across the samples for the same gene, it can be corrected by proper experimental design and analysis. If the dye bias is not consistent across samples for the same gene, but is different for different samples, then removing the bias becomes more problematic, perhaps indicating a technical limitation to the ability of fluorescent signals to accurately represent gene expression. Thus, it is important to characterize dye bias to determine: (1) whether it will be removed for all genes by array normalization, (2) whether it will not be removed by normalization but can be removed by proper experimental design and analysis and (3) whether dye bias correction is more problematic than either of these and is not easily removable. For two dual-label experiments, one with cDNA arrays and the other with printed oligonucleotide arrays, Stratagene universal human reference RNA was used as a standard for testing with RNA from cell lines MCF10a, LNCAP, L428, SUDHL, OCILY3 and Jurkat. All arrays were dye-swapped at least twice. There were a total of 28 cDNA arrays and 30 oligonucleotide arrays.
2006-06-06 | E-GEOD-4982 | biostudies-arrayexpress
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2005-07-02 | GSE2520 | GEO
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2013-12-04 | GSE51538 | GEO
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