Interpretation of custom designed Illumina genotype cluster plots for targeted association studies and next-generation sequence validation

Abstract

BackgroundHigh-throughput custom designed genotyping arrays are a valuable resource for biologically focused research studies and increasingly for validation of variation predicted by next-generation sequencing (NGS) technologies. We investigate the Illumina GoldenGate chemistry using custom designed VeraCode and sentrix array matrix (SAM) assays for each of these applications, respectively. We highlight applications for interpretation of Illumina generated genotype cluster plots to maximise data inclusion and reduce genotyping errors.FindingsWe illustrate the dramatic effect of outliers in genotype calling and data interpretation, as well as suggest simple means to avoid genotyping errors. Furthermore we present this platform as a successful method for two-cluster rare or non-autosomal variant calling. The success of high-throughput technologies to accurately call rare variants will become an essential feature for future association studies. Finally, we highlight additional advantages of the Illumina GoldenGate chemistry in generating unusually segregated cluster plots that identify potential NGS generated sequencing error resulting from minimal coverage.ConclusionsWe demonstrate the importance of visually inspecting genotype cluster plots generated by the Illumina software and issue warnings regarding commonly accepted quality control parameters. In addition to suggesting applications to minimise data exclusion, we propose that the Illumina cluster plots may be helpful in identifying potential in-put sequence errors, particularly important for studies to validate NGS generated variation.

Fish scales and SNP chips: SNP genotyping and allele frequency estimation in individual and pooled DNA from historical samples of Atlantic salmon (Salmo salar)

“…Loci classified as ‘MSV-3′ (where a SNP exists on a single paralogue; N = 873), ‘Mono’ (N = 516, where loci in Lien et al 2011 were monomorphic) and ‘SNP’ (segregates as a normal diploid SNP, N = 3928) were retained (a combined total of 5317 loci), whereas loci with classifications ‘Unknown’, ‘MSV-5′, ‘PSV’ , ‘Mito’ and ‘Failed’ were discarded (see [40] for further discussion on locus classification). Variation in the quality and quality of DNA samples and hybridisation efficiency between loci can lead to differences in cluster positions between studies [41]. In particular, MSV-3 locus genotypes cluster more tightly and are not always accurately defined by the GenomeStudio software (Additional file 1: Figure S1).…”

Contribution of genetic background and antiretroviral therapy to body fat changes in antiretroviral-naive HIV-infected adults

“…Duplicates from 10% of the samples were regenotyped as a genotyping quality control. The resulting genotypes were filtered prior to statistical analysis: 36 Subjects with a genotyping call rate ,80% and SNPs with a genotyping call rate ,95% were excluded from the analysis.…”

Population Diversity of Leptosphaeria maculans in Australia

“…The Illumina GoldenGate genotyping assay can be used to simultaneously analyse 384-3072 SNP loci across multiple individuals (Tindall et al, 2010). Previous studies using the Illumina GoldenGate assay have shown that it can be used to reliably score SNPs for genetic analysis (Durstewitz et al, 2010).…”