Introduction

Array based genotyping provide a powerful tool to interrogate genetic variation. It enables a broad variety of applications such as genome-wide association studies, evaluation of genetic merit in breeding applications, linkage disequilibrium studies, comparative genetic studies, as well as for characterizing biodiversity [1]. High density genotyping arrays are produced for a number of species having a reference genome available, and the SNPs on the arrays are mapped to genomic positions. For convenience and standardization the mapping data is distributed along with the arrays to the end user. Apart from crude positional information, the mapping position provides the basis for annotation of potential effects of the SNP alleles upon RNA splicing, regulatory elements or amino acid exchanges. Furthermore, the relative order of the SNPs may in some cases effect data analysis and generation of haplotypes, imputation as well as linkage studies. We here report that minor SNP positional discrepancies exist in various Affymetrix™ and Illumina™ genotyping arrays made for human, mouse and cow species, and discuss the possible functional consequences.

Results

To detect genomic position discrepancies of SNPs in genotyping arrays, we used BLAST [2] with highly sensitive parameter settings, and restricted the analysis to examine only SNPs that mapped with a unique perfect match to the species genome in question (Methods).

In order to explain possible differences in mapping we chose to compare first our mappings of the BovineSNP50 v1 beadchip (http://www.illumina.com/products/bovine_snp50_whole-genome_genotyping_kits.ilmn) with the ones made by Illumina™. From the 54,001 SNPs present on the chip, we found that only 41,496 (77%) had a unique and perfect match in both our and Illumina™ mappings (Data S1). Illumina™ reports the unique mapping of 52,255 SNPs while we mapped 10% less (46,760) using the same genome assembly (Bt4.0). The difference can be explained by the fact that Illumina™ did not use the unassembled chromosome (ChrUn, which consists of almost 10% of genomic sequence of the assembly) as part of their mapping process. Using 41,496 uniquely mapped SNPs by both and omitting mapping differences if they were on ChrUn, we detected 99 SNP genomic position discrepancies, of which 16 (17%) are in different genes. These 99 differences lead to a change in genomic order of 7,209 SNPs, with 99% being less or equal than 2 indexes away (Data S1).

Next, we examined the discrepancies between our mappings and the ones made by a study that previously reported mappings differences relative to the Illumina™ BovineSNP50 [3]. As before, we used SNPs found to be uniquely mapped in both studies. From 41,536 SNPs, we detected 764 differences (Data S1). By manually checking a fraction of those alignments we noted that, albeit their mapping procedure is correct, their post-processing script leads to calling SNPs 1 bp upstream or downstream of the actual SNP genomic position (Data S1). It should be noted that while we used BLAST [2] in the present study the previous report used MEGABLAST [4]. Using a shorter word size (9 versus 28), our search is likely to result in a better alignment sensitivity (a mapping file with the updated SNP positions is supplied as Data S1).

Recently, a new version of the BovineSNP50 beadchip (v2) came into the market which includes 54,609 SNPs in comparison to 54,001 SNPs from the previous version. Using the same procedure we mapped the SNP postions for this beadchip using only the SNPs that have a unique perfect hit in the genome assembly (UMD3.1 in this case). From 48,284 SNPs, we detected 449 SNP genomic position discrepancies, of which 248 (55%) were in different genes. These 449 discrepancies lead to a change in the genomic order of 13,133 SNPs, with 90% being less or equal than 2 index positions away (updated mapping file provided as Data S2).

Having identified discrepancies in the mapping of Illumina's™ BovineSNP50 beadchips, we decided to study a number of other high-density genotyping arrays. First, we analyzed the BovineHD beadchip (http://www.illumina.com/products/bovinehd_whole-genome_genotyping_kits.ilmn), and found that only 14 SNPs (2 in different chromosomes) retrieved mapping to different genomic positions, of which 2 (14%) were observed in different genes. These 14 discrepancies lead to a change in genomic order of 182 SNPs, with 98% being less or equal than 1 index away (Data S3). By manually checking these 14 mapping discrepancies, we found three reasons for the Illumina™ mismapping: (1) presence of an extra SNP on the SNP flanking sequence, (2) or having less flanking sequence aligned, or (3) mapped to 1 bp apart near the actual SNP position.

Next, we analyzed the Affymetrix™ mouse diversity genotyping array (http://media.affymetrix.com/support/technical/datasheets/mouse_diversity_array_datasheet.pdf). We detected that 620 SNPs (5 in different chromosomes) retrieved different genomic mapping positions, of which 66 (11%) are in different genes. These 620 differences lead to a change in genomic order of 271,325 SNPs, with 98% being less or equal than 2 indexes away (Data S4). By manually checking a fraction of these discrepancies, we found that those Affymetrix™ mismappings are either due to their hit not being perfect, or having one or more extra SNPs on the SNP flanking sequence.

Our next step was to detect SNP position discrepancies in two of the most widely used genotyping arrays in human studies: Illumina's™ Human1M-Duo DNA Analysis beadchip (http://www.illumina.com/products/human1m_duo_dna_analysis_beadchip_kits.ilmn) and the Affymetrix's™ Genome-Wide Human SNP Array 6.0 (http://media.affymetrix.com/support/technical/datasheets/genomewide_snp6_datasheet.pdf). Concerning the Affymetrix™ human genotyping array, we detected 25 differences (5 in different chromosomes), of which 10 (40%) were in different genes. These 25 discrepancies lead to 61,916 SNPs being in a different genomic order, with 89% being less or equal than 2 indexes away (Data S5). By checking manually a subset of those alignment discrepancies, we found that the Affymetrix™ mismappings were due to their hit not being perfect, having one or more extra SNPs on the SNP flanking sequence, or having less flanking sequence aligned. With the Illumina's™ human beadchip, 271 SNPs have different genomic positions (with 22 on different chromosomes), of which 59 (22%) are on different genes. The 271 discrepancies lead to 131,378 SNPs being in a different genomic order, although 98% are less or equal than two indexes away (Data S6). By checking manually a subset of those alignment discrepancies, we found that the Illumina™ mismappings were due to their hit not being perfect, having one or more extra SNPs on the SNP flanking sequence, or having less flanking sequence aligned.

Furthermore, we were interested in a more detailed understanding of the possible functional impact of relying on incorrectly mapped positions. Therefore, we searched through a selection of papers published in 2010 for SNPs associated with various human phenotypes and found that two of the SNPs with positional discrepancies on the Illumina's™ Human1M-Duo DNA Analysis beadchip were reported to be significantly correlated with human traits in recent genome wide association studies [5]–[7]. The first, rs2523608, has a positional discrepancy of only 1 bp but since it is in an intronic region of a gene it might be enough to trigger different an erroneous prediction functional consequences. It is shown to be significant in two papers from 2010 related to HIV [5]–[6]. The second, rs9692809, has a positional discrepancy of 697 kb and does overlap a hypothetical gene in the Illumina™ mapping but not in ours. It is shown to be significantly associated with vertical optic cup-to-disc ratio [7]. Figure 1 shows the alignment of this SNP by both Illumina™ and our own mapping data.

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Figure 1. Example of a wrongly mapped SNP found significant in a GWAS (Macgregor et al. 2010).

The red SNP shown in the circular human karyotype diagram instead of being mapped to position 7,873,470 in chr 8 (Rank1), it is wrongly mapped by Illumina™ to position 7,176,768 in chr8 (Rank2), with less 7 bp of the SNP flanking sequence aligned.

https://doi.org/10.1371/journal.pone.0031025.g001

Discussion

In summary, despite most SNPs map reliably and consistently and most changes in position are relatively localized, the SNP discrepancies found in this report clearly suggest that more sensitive parameters of the aligners (whether they are BLAST, MegaBLAST or other) should be used in order to achieve an accurate chromosomal alignment instead of retrieving a partial best alignment with extra SNPs, indels or less SNP flanking sequence aligned (Table 1).

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Table 1. Summary of the chromosomal SNP position discrepancies for each SNP genotyping platform tested.

https://doi.org/10.1371/journal.pone.0031025.t001

Since wrongly mapped SNPs can change in which genic and regulatory regions they overlap, it can trigger erroneous variant effect conclusions. Large SNP positional discrepancies can also affect studies of genotype imputation and linkage disequilibrium, leading to false coverage and power of genome-wide association analysis and erroneous evaluation of the choice of SNP platform to use [8]. Our study here do not intend to cast doubt on the main conclusions of any paper, but rather intend to ensure that future studies use the correct chromosomal SNP positions in order to minimize erroneous conclusions.

We would recommend the providers of commercial SNP chips to always provide (for each chip) a technical report on how they exactly did the mapping. Specifically, refer to which mapping algorithm and its parameters used, genome assembly version, and the location of SNP flanking sequences in their websites. It was our experience that trying to retrieve this information revealed to be a cumbersome task, with little or no information provided regarding the SNP mapping procedure.

In Supplementary data we provide our mappings for the genotyping platforms tested here, and we hope that investigators using different genotyping platforms are encouraged to map them using an accurate and sensitive procedure (Methods). The SNPs that map to multiple regions can also be easily retrieved from public databases such as dbSNP, UCSC or Ensembl. These SNPs most probably map to paralogous regions of the genome with high sequence identity [9]–[10].

Methods

Supporting Information