Strategies for processing and quality control of Illumina genotyping arrays.

Illumina genotyping arrays have powered thousands of large-scale genome-wide association studies over the past decade. Yet, because of the tremendous volume and complicated genetic assumptions of Illumina genotyping data, processing and quality control (QC) of these data remain a challenge. Thorough QC ensures the accurate identification of single-nucleotide polymorphisms and is required for the correct interpretation of genetic association results. By processing genotyping data on > 100 000 subjects from >10 major Illumina genotyping arrays, we have accumulated extensive experience in handling some of the most peculiar scenarios related to the processing and QC of Illumina genotyping data. Here, we describe strategies for processing Illumina genotyping data from the raw data to an analysis ready format, and we elaborate on the necessary QC procedures required at each processing step. High-quality Illumina genotyping data sets can be obtained by following our detailed QC strategies.

A micro-RNA expression signature for human NAFLD progression.

BACKGROUND: The spectrum of nonalcoholic fatty liver disease (NAFLD) describes disease conditions deteriorating from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH) to cirrhosis (CIR) to hepatocellular carcinoma (HCC). From a molecular and biochemical perspective, our understanding of the etiology of this disease is limited by the broad spectrum of disease presentations, the lack of a thorough understanding of the factors contributing to disease susceptibility, and ethical concerns related to repeat sampling of the liver. To better understand the factors associated with disease progression, we investigated by next-generation RNA sequencing the altered expression of microRNAs (miRNAs) in liver biopsies of class III obese subjects (body mass index ≥40 kg/m(2)) biopsied at the time of elective bariatric surgery. METHODS: Clinical characteristics and unbiased RNA expression profiles for 233 miRs, 313 transfer RNAs (tRNAs), and 392 miscellaneous small RNAs (snoRNAs, snRNAs, rRNAs) were compared among 36 liver biopsy specimens stratified by disease severity. RESULTS: The abundances of 3 miRNAs that were found to be differentially regulated (miR-301a-3p and miR-34a-5p increased and miR-375 decreased) with disease progression were validated by RT-PCR. No tRNAs or miscellaneous RNAs were found to be associated with disease severity. Similar patterns of increased miR-301a and decreased miR-375 expression were observed in 134 hepatocellular carcinoma (HCC) samples deposited in The Cancer Genome Atlas (TCGA). CONCLUSIONS: Our analytical results suggest that NAFLD severity is associated with a specific pattern of altered hepatic microRNA expression that may drive the hallmark of this disorder: altered lipid and carbohydrate metabolism. The three identified miRNAs can potentially be used as biomarkers to access the severity of NAFLD. The persistence of this miRNA expression pattern in an external validation cohort of HCC samples suggests that specific microRNA expression patterns may permit and/or sustain NAFLD development to HCC.

Three-stage quality control strategies for DNA re-sequencing data.

Advances in next-generation sequencing (NGS) technologies have greatly improved our ability to detect genomic variants for biomedical research. In particular, NGS technologies have been recently applied with great success to the discovery of mutations associated with the growth of various tumours and in rare Mendelian diseases. The advance in NGS technologies has also created significant challenges in bioinformatics. One of the major challenges is quality control of the sequencing data. In this review, we discuss the proper quality control procedures and parameters for Illumina technology-based human DNA re-sequencing at three different stages of sequencing: raw data, alignment and variant calling. Monitoring quality control metrics at each of the three stages of NGS data provides unique and independent evaluations of data quality from differing perspectives. Properly conducting quality control protocols at all three stages and correctly interpreting the quality control results are crucial to ensure a successful and meaningful study.

Comparative study of exome copy number variation estimation tools using array comparative genomic hybridization as control.

Exome sequencing using next-generation sequencing technologies is a cost-efficient approach to selectively sequencing coding regions of the human genome for detection of disease variants. One of the lesser known yet important applications of exome sequencing data is to identify copy number variation (CNV). There have been many exome CNV tools developed over the last few years, but the performance and accuracy of these programs have not been thoroughly evaluated. In this study, we systematically compared four popular exome CNV tools (CoNIFER, cn.MOPS, exomeCopy, and ExomeDepth) and evaluated their effectiveness against array comparative genome hybridization (array CGH) platforms. We found that exome CNV tools are capable of identifying CNVs, but they can have problems such as high false positives, low sensitivity, and duplication bias when compared to array CGH platforms. While exome CNV tools do serve their purpose for data mining, careful evaluation and additional validation is highly recommended. Based on all these results, we recommend CoNIFER and cn.MOPs for nonpaired exome CNV detection over the other two tools due to a low false-positive rate, although none of the four exome CNV tools performed at an outstanding level when compared to array CGH.