Quality Control for the Illumina HumanExome BeadChip

Identification and functional validation of HLA-C as a potential gene involved in colorectal cancer in the Korean population

BACKGROUND

Colorectal cancer (CRC) is the third most common cancer worldwide and is influenced by environmental and genetic factors. Although numerous genetic loci for CRC have been identified, the overall understanding of the genetic factors is yet to be elucidated. We sought to discover new genes involved in CRC applying genetic association analysis and functional study.

RESULTS

We conducted exome array analysis on 194 CRC and 600 control subjects for discovering new candidate CRC genes. Fisher's exact test detected one exome-wide significant functional locus for CRC on SMCO1 (P < 10^-6) and two suggestive functional loci on HLA-C and NUTM1 (10^-6 ≤ P < 10^-4). To evaluate the biological role of three candidate CRC genes, the differential expression of these genes between CRC and non-cancer colorectal cells was analyzed using qRT-PCR and publicly available gene expression data. Of three genes, HLA-C consistently revealed the significant down-regulation in CRC cells. In addition, we detected a reduction in cell viability in the HLA-C overexpression CRC cell line, implying the functional relevance of HLA-C in CRC. To understand the underlying mechanism exerted by HLA-C in CRC development, we conducted RNA sequencing analyses of HLA-C overexpression CRC cells and non-cancer colorectal cells. Pathway analysis detected that significantly down-regulated genes in HLA-C overexpression CRC cells were highly enriched in cancer-related signaling pathways such as JAK/STAT, ErbB, and Hedgehog signaling pathways.

CONCLUSIONS

Exome array CRC case-control analysis followed by functional validation demonstrated that HLA-C likely exerts its influence on CRC development via cancer-related signaling pathways.

Establishing analytical validity of BeadChip array genotype data by comparison to whole-genome sequence and standard benchmark datasets

BACKGROUND

Clinical use of genotype data requires high positive predictive value (PPV) and thorough understanding of the genotyping platform characteristics. BeadChip arrays, such as the Global Screening Array (GSA), potentially offer a high-throughput, low-cost clinical screen for known variants. We hypothesize that quality assessment and comparison to whole-genome sequence and benchmark data establish the analytical validity of GSA genotyping.

METHODS

To test this hypothesis, we selected 263 samples from Coriell, generated GSA genotypes in triplicate, generated whole genome sequence (rWGS) genotypes, assessed the quality of each set of genotypes, and compared each set of genotypes to each other and to the 1000 Genomes Phase 3 (1KG) genotypes, a performance benchmark. For 59 genes (MAP59), we also performed theoretical and empirical evaluation of variants deemed medically actionable predispositions.

RESULTS

Quality analyses detected sample contamination and increased assay failure along the chip margins. Comparison to benchmark data demonstrated that > 82% of the GSA assays had a PPV of 1. GSA assays targeting transitions, genomic regions of high complexity, and common variants performed better than those targeting transversions, regions of low complexity, and rare variants. Comparison of GSA data to rWGS and 1KG data showed > 99% performance across all measured parameters. Consistent with predictions from prior studies, the GSA detection of variation within the MAP59 genes was 3/261.

CONCLUSION

We establish the analytical validity of GSA assays using quality analytics and comparison to benchmark and rWGS data. GSA assays meet the standards of a clinical screen although assays interrogating rare variants, transversions, and variants within low-complexity regions require careful evaluation.

Genome-Wide Association Study of Metamizole-Induced Agranulocytosis in European Populations

Agranulocytosis is a rare yet severe idiosyncratic adverse drug reaction to metamizole, an analgesic widely used in countries such as Switzerland and Germany. Notably, an underlying mechanism has not yet been fully elucidated and no predictive factors are known to identify at-risk patients. With the aim to identify genetic susceptibility variants to metamizole-induced agranulocytosis (MIA) and neutropenia (MIN), we conducted a retrospective multi-center collaboration including cases and controls from three European populations. Association analyses were performed using genome-wide genotyping data from a Swiss cohort (45 cases, 191 controls) followed by replication in two independent European cohorts (41 cases, 273 controls) and a joint discovery meta-analysis. No genome-wide significant associations (p < 1 × 10⁻⁷) were observed in the Swiss cohort or in the joint meta-analysis, and no candidate genes suggesting an immune-mediated mechanism were identified. In the joint meta-analysis of MIA cases across all cohorts, two candidate loci on chromosome 9 were identified, rs55898176 (OR = 4.01, 95%CI: 2.41–6.68, p = 1.01 × 10⁻⁷) and rs4427239 (OR = 5.47, 95%CI: 2.81–10.65, p = 5.75 × 10⁻⁷), of which the latter is located in the SVEP1 gene previously implicated in hematopoiesis. This first genome-wide association study for MIA identified suggestive associations with biological plausibility that may be used as a stepping-stone for post-GWAS analyses to gain further insight into the mechanism underlying MIA.

PLINK: Key Functions for Data Analysis

Genetic data analysis of large numbers of single nucleotide variants (SNVs), including genome-wide association studies (GWAS), exome chips, and whole exome (WES) or whole-genome (WGS) sequencing data, requires well defined processing steps. As a result, several freely available analytic toolkits have been developed to streamline these processes. Among these, PLINK is the most comprehensive in terms of its quality control and analytic modules, although its focus remains on SNVs. PLINK fulfills two analytic needs-aiding the process of performing quality control (QC) on large data sets and providing basic statistical tools to analyze the variants in genetic models. The current version of PLINK (v1.90b) has incorporated several sophisticated statistical modeling features, such as those that were introduced by GCTA (genome-wide complex trait analysis), including mixed-model association analysis and cluster-based algorithms. Although PLINK is diverse in its applicability to data management and analysis, in some instances, other available tools offer more optimal options. Here we provide a practical overview of major PLINK features with respect to QC, data management, and association mapping, along with learned shortcuts and limitations to be considered. In cases where PLINK features are limited, we provide alternative approaches using additional freely available pipelines. © 2018 by John Wiley & Sons, Inc.