Comprehensive, integrated, and phased whole-genome analysis of the primary ENCODE cell line K562.

K562 is widely used in biomedical research. It is one of three tier-one cell lines of ENCODE and also most commonly used for large-scale CRISPR/Cas9 screens. Although its functional genomic and epigenomic characteristics have been extensively studied, its genome sequence and genomic structural features have never been comprehensively analyzed. Such information is essential for the correct interpretation and understanding of the vast troves of existing functional genomics and epigenomics data for K562. We performed and integrated deep-coverage whole-genome (short-insert), mate-pair, and linked-read sequencing as well as karyotyping and array CGH analysis to identify a wide spectrum of genome characteristics in K562: copy numbers (CN) of aneuploid chromosome segments at high-resolution, SNVs and indels (both corrected for CN in aneuploid regions), loss of heterozygosity, megabase-scale phased haplotypes often spanning entire chromosome arms, structural variants (SVs), including small and large-scale complex SVs and nonreference retrotransposon insertions. Many SVs were phased, assembled, and experimentally validated. We identified multiple allele-specific deletions and duplications within the tumor suppressor gene FHIT Taking aneuploidy into account, we reanalyzed K562 RNA-seq and whole-genome bisulfite sequencing data for allele-specific expression and allele-specific DNA methylation. We also show examples of how deeper insights into regulatory complexity are gained by integrating genomic variant information and structural context with functional genomics and epigenomics data. Furthermore, using K562 haplotype information, we produced an allele-specific CRISPR targeting map. This comprehensive whole-genome analysis serves as a resource for future studies that utilize K562 as well as a framework for the analysis of other cancer genomes.

Whole-genome sequencing analysis of CNV using low-coverage and paired-end strategies is efficient and outperforms array-based CNV analysis.

BACKGROUND: Copy number variation (CNV) analysis is an integral component of the study of human genomes in both research and clinical settings. Array-based CNV analysis is the current first-tier approach in clinical cytogenetics. Decreasing costs in high-throughput sequencing and cloud computing have opened doors for the development of sequencing-based CNV analysis pipelines with fast turnaround times. We carry out a systematic and quantitative comparative analysis for several low-coverage whole-genome sequencing (WGS) strategies to detect CNV in the human genome. METHODS: We compared the CNV detection capabilities of WGS strategies (short insert, 3 kb insert mate pair and 5 kb insert mate pair) each at 1×, 3× and 5× coverages relative to each other and to 17 currently used high-density oligonucleotide arrays. For benchmarking, we used a set of gold standard (GS) CNVs generated for the 1000 Genomes Project CEU subject NA12878. RESULTS: Overall, low-coverage WGS strategies detect drastically more GS CNVs compared with arrays and are accompanied with smaller percentages of CNV calls without validation. Furthermore, we show that WGS (at ≥1× coverage) is able to detect all seven GS deletion CNVs >100 kb in NA12878, whereas only one is detected by most arrays. Lastly, we show that the much larger 15 Mbp Cri du chat deletion can be readily detected with short-insert paired-end WGS at even just 1× coverage. CONCLUSIONS: CNV analysis using low-coverage WGS is efficient and outperforms the array-based analysis that is currently used for clinical cytogenetics.

Comprehensive performance comparison of high-resolution array platforms for genome-wide Copy Number Variation (CNV) analysis in humans.

BACKGROUND: High-resolution microarray technology is routinely used in basic research and clinical practice to efficiently detect copy number variants (CNVs) across the entire human genome. A new generation of arrays combining high probe densities with optimized designs will comprise essential tools for genome analysis in the coming years. We systematically compared the genome-wide CNV detection power of all 17 available array designs from the Affymetrix, Agilent, and Illumina platforms by hybridizing the well-characterized genome of 1000 Genomes Project subject NA12878 to all arrays, and performing data analysis using both manufacturer-recommended and platform-independent software. We benchmarked the resulting CNV call sets from each array using a gold standard set of CNVs for this genome derived from 1000 Genomes Project whole genome sequencing data. RESULTS: The arrays tested comprise both SNP and aCGH platforms with varying designs and contain between ~0.5 to ~4.6 million probes. Across the arrays CNV detection varied widely in number of CNV calls (4-489), CNV size range (~40 bp to ~8 Mbp), and percentage of non-validated CNVs (0-86%). We discovered strikingly strong effects of specific array design principles on performance. For example, some SNP array designs with the largest numbers of probes and extensive exonic coverage produced a considerable number of CNV calls that could not be validated, compared to designs with probe numbers that are sometimes an order of magnitude smaller. This effect was only partially ameliorated using different analysis software and optimizing data analysis parameters. CONCLUSIONS: High-resolution microarrays will continue to be used as reliable, cost- and time-efficient tools for CNV analysis. However, different applications tolerate different limitations in CNV detection. Our study quantified how these arrays differ in total number and size range of detected CNVs as well as sensitivity, and determined how each array balances these attributes. This analysis will inform appropriate array selection for future CNV studies, and allow better assessment of the CNV-analytical power of both published and ongoing array-based genomics studies. Furthermore, our findings emphasize the importance of concurrent use of multiple analysis algorithms and independent experimental validation in array-based CNV detection studies.

Exome sequencing and genome-wide copy number variant mapping reveal novel associations with sensorineural hereditary hearing loss.

BACKGROUND: The genetic diversity of loci and mutations underlying hereditary hearing loss is an active area of investigation. To identify loci associated with predominantly non-syndromic sensorineural hearing loss, we performed exome sequencing of families and of single probands, as well as copy number variation (CNV) mapping in a case-control cohort. RESULTS: Analysis of three distinct families revealed several candidate loci in two families and a single strong candidate gene, MYH7B, for hearing loss in one family. MYH7B encodes a Type II myosin, consistent with a role for cytoskeletal proteins in hearing. High-resolution genome-wide CNV analysis of 150 cases and 157 controls revealed deletions in genes known to be involved in hearing (e.g. GJB6, OTOA, and STRC, encoding connexin 30, otoancorin, and stereocilin, respectively), supporting CNV contributions to hearing loss phenotypes. Additionally, a novel region on chromosome 16 containing part of the PDXDC1 gene was found to be frequently deleted in hearing loss patients (OR=3.91, 95% CI: 1.62-9.40, p=1.45×10(-7)). CONCLUSIONS: We conclude that many known as well as novel loci and distinct types of mutations not typically tested in clinical settings can contribute to the etiology of hearing loss. Our study also demonstrates the challenges of exome sequencing and genome-wide CNV mapping for direct clinical application, and illustrates the need for functional and clinical follow-up as well as curated open-access databases.

Impacts of variation in the human genome on gene regulation.

Recent advances in fast and inexpensive DNA sequencing have enabled the extensive study of genomic and transcriptomic variation in humans. Human genomic variation is composed of sequence and structural changes including single-nucleotide and multinucleotide variants, short insertions or deletions (indels), larger copy number variants, and similarly sized copy neutral inversions and translocations. It is now well established that any two genomes differ extensively and that structural changes constitute a prominent source of this variation. There have also been major technological advances in RNA sequencing to globally quantify and describe diversity in transcripts. Large consortia such as the 1000 Genomes Project and the ENCODE (ENCyclopedia Of DNA Elements) Project are producing increasingly comprehensive maps outlining the regions of the human genome containing variants and functional elements, respectively. Integration of genetic variation data and extensive annotation of functional genomic elements, along with the ability to measure global transcription, allow the impacts of genetic variants on gene expression to be resolved. There are several well-established models by which genetic variants affect gene regulation depending on the type, nature, and position of the variant with respect to the affected genes. These effects can be manifested in two ways: changes to transcript sequences and isoforms by coding variants, and changes to transcript abundance by dosage or regulatory variants. Here, we review the current state of how genetic variations impact gene regulation locally and globally in the human genome.

Genome-wide mapping of copy number variation in humans: comparative analysis of high resolution array platforms.

Accurate and efficient genome-wide detection of copy number variants (CNVs) is essential for understanding human genomic variation, genome-wide CNV association type studies, cytogenetics research and diagnostics, and independent validation of CNVs identified from sequencing based technologies. Numerous, array-based platforms for CNV detection exist utilizing array Comparative Genome Hybridization (aCGH), Single Nucleotide Polymorphism (SNP) genotyping or both. We have quantitatively assessed the abilities of twelve leading genome-wide CNV detection platforms to accurately detect Gold Standard sets of CNVs in the genome of HapMap CEU sample NA12878, and found significant differences in performance. The technologies analyzed were the NimbleGen 4.2 M, 2.1 M and 3×720 K Whole Genome and CNV focused arrays, the Agilent 1×1 M CGH and High Resolution and 2×400 K CNV and SNP+CGH arrays, the Illumina Human Omni1Quad array and the Affymetrix SNP 6.0 array. The Gold Standards used were a 1000 Genomes Project sequencing-based set of 3997 validated CNVs and an ultra high-resolution aCGH-based set of 756 validated CNVs. We found that sensitivity, total number, size range and breakpoint resolution of CNV calls were highest for CNV focused arrays. Our results are important for cost effective CNV detection and validation for both basic and clinical applications.