Group-based variant calling leveraging next-generation supercomputing for large-scale whole-genome sequencing studies

Standish, Kristopher A.; Carland, Tristan M.; Lockwood, Glenn K.; Pfeiffer, Wayne; Tatineni, Mahidhar; Huang, Chaohua; Lamberth, S.; Cherkas, Yauheniya; Brodmerkel, Carrie; Jaeger, Ed; Smith, Lance; Rajagopal, G.; Curran, Mark; Schork, Nicholas J.

doi:10.1186/s12859-015-0736-4

Cited by 12 publications

(15 citation statements)

References 16 publications

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“…A summary of patients used in this study is given in Table 1 . Transcriptome data (Affymetrix microarray) from whole blood were compared to matching genotypes generated from whole-genome sequencing [ 17 ]. In brief, eQTL mapping was performed by linear regression on adjusted data and false discovery rate (FDR) was estimated with a permutation method separately for local ( cis defined as less than 1 Mb distance from SNP to gene) and distant associations ( trans , greater than 1 Mb from SNP to gene including across chromosomes; see “ Methods ”).…”

Section: Resultsmentioning

confidence: 99%

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Integrative genomic deconvolution of rheumatoid arthritis GWAS loci into gene and cell type associations

et al. 2016

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BackgroundAlthough genome-wide association studies (GWAS) have identified over 100 genetic loci associated with rheumatoid arthritis (RA), our ability to translate these results into disease understanding and novel therapeutics is limited. Most RA GWAS loci reside outside of protein-coding regions and likely affect distal transcriptional enhancers. Furthermore, GWAS do not identify the cell types where the associated causal gene functions. Thus, mapping the transcriptional regulatory roles of GWAS hits and the relevant cell types will lead to better understanding of RA pathogenesis.ResultsWe combine the whole-genome sequences and blood transcription profiles of 377 RA patients and identify over 6000 unique genes with expression quantitative trait loci (eQTLs). We demonstrate the quality of the identified eQTLs through comparison to non-RA individuals. We integrate the eQTLs with immune cell epigenome maps, RA GWAS risk loci, and adjustment for linkage disequilibrium to propose target genes of immune cell enhancers that overlap RA risk loci. We examine 20 immune cell epigenomes and perform a focused analysis on primary monocytes, B cells, and T cells.ConclusionsWe highlight cell-specific gene associations with relevance to RA pathogenesis including the identification of FCGR2B in B cells as possessing both intragenic and enhancer regulatory GWAS hits. We show that our RA patient cohort derived eQTL network is more informative for studying RA than that from a healthy cohort. While not experimentally validated here, the reported eQTLs and cell type-specific RA risk associations can prioritize future experiments with the goal of elucidating the regulatory mechanisms behind genetic risk associations.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-0948-6) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

“…Genotypes were generated from whole-genome sequencing as described in Standish et al [ 17 ]. DNA isolated from whole blood was sequenced and DNA variants called using a modified GATK pipeline.…”

Section: Methodsmentioning

confidence: 99%

Integrative genomic deconvolution of rheumatoid arthritis GWAS loci into gene and cell type associations

et al. 2016

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“…While group-calling is the approach recommended by GATK, with the exception of one paper from our lab exploring the impact of genetic background on group calling, to our knowledge there has not been a published systematic comparison of group calling vs. single sample calling on a gold standard dataset to quantify the advantages of group calling [30]. Group-calling is not without problems.…”

Section: Discussionmentioning

confidence: 99%

Pan-Cancer Analysis Reveals Technical Artifacts in TCGA Germline Variant Calls

Buckley

Standish

Bhutani

et al. 2016

Preprint

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The degree to which germline variation drives cancer development and shapes tumor phenotypes remains largely unexplored, possibly due to a lack of large scale publicly available germline data for a cancer cohort. Here we called germline variants on 9,618 cases from The Cancer Genome Atlas (TCGA) database representing 31 cancer types. We identified batch effects affecting loss of function (LOF) variant calls that can be traced back to differences in the way the sequence data were generated both within and across cancer types. Overall, LOF indel calls were more sensitive to technical artifacts than LOF Single Nucleotide Variant (SNV) calls. In particular, whole genome amplification of DNA prior to sequencing led to an artificially increased burden of LOF indel calls, which confounded association analyses relating germline variants to tumor type despite stringent indel filtering strategies. Due to the inherent noise we chose to remove all 614 amplified DNA samples, including all acute myeloid leukemia and virtually all ovarian cancer samples, from the final dataset. This study demonstrates how insufficient quality control can lead to false positive germlinetumor type associations and draws attention to the need to be sensitive to problems associated with a lack of uniformity in data generation in TCGA data. There are substantial differences in the way exome sequence data was generated both across and within cancer types in TCGA. We observe that differences in sequence data generation introduced batch effects, or variation that is due to technical factors not true biological variation, in our variant data. Most notably, we observe that amplification of DNA prior to sequencing resulted in an excess of predicted damaging indel variants. We show how these batch effects can confound germline association analyses if not properly addressed. Our study highlights the difficulties of working with large public genomic datasets like TCGA where samples are collected over time and across data centers, and particularly cautions the use of amplified DNA samples for genetic association analyses.

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“…Several uses of BBs have been shown in order to mitigate the I/O bottlenecks of data-intensive workloads [6,32,33,34]. Most studies surrounding the design and use of BBs have so far focused on the I/O characteristics of individual applications [35] or small components within workflows [4]. However, research into optimizing scientific workflows with diverse I/O and storage requirements for BBs is still in its infancy, and a limited body of work presently exists [3,36].…”

Section: Related Workmentioning

confidence: 99%

“…Thus, while providers of supercomputing resources must continue to support the extreme bandwidth requirements of traditional supercomputing applications, centers must now also deploy resources that are capable of supporting the requirements of these emerging data-intensive workflows. In sharp contrast to the highly coherent, sequential, large-transaction reads and writes that are characteristic of traditional HPC checkpoint-restart workloads [2], dataintensive workflows have been shown to often utilize non-sequential, metadataintensive, and small-transaction reads and writes [3,4]. However, parallel file systems in today's supercomputers have been optimized for more traditional HPC workloads [5].…”

Section: Introductionmentioning

confidence: 99%

Performance characterization of scientific workflows for the optimal use of Burst Buffers

Daley

Ghoshal

Lockwood

et al. 2020

Future Generation Computer Systems

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Scientific discoveries are increasingly dependent upon the analysis of large volumes of data from observations and simulations of complex phenomena. Scientists compose the complex analyses as workflows and execute them on large-scale HPC systems. The workflow structures are in contrast with monolithic single simulations that have often been the primary use case on HPC systems. Simultaneously, new storage paradigms such as Burst Buffers are becoming available on HPC platforms. In this paper, we analyze the performance characteristics of a Burst Buffer and two representative scientific workflows with the aim of optimizing the usage of a Burst Buffer, extending our previous analyses [1]. Our key contributions are a). developing a performance analysis methodology pertinent to Burst Buffers, b). improving the use of a Burst Buffer in workflows with bandwidth-sensitive and metadata-sensitive I/O workloads, c). highlighting the key data management challenges when incorporating a Burst Buffer in the studied scientific workflows.

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Group-based variant calling leveraging next-generation supercomputing for large-scale whole-genome sequencing studies

Cited by 12 publications

References 16 publications

Integrative genomic deconvolution of rheumatoid arthritis GWAS loci into gene and cell type associations

Integrative genomic deconvolution of rheumatoid arthritis GWAS loci into gene and cell type associations

Pan-Cancer Analysis Reveals Technical Artifacts in TCGA Germline Variant Calls

Performance characterization of scientific workflows for the optimal use of Burst Buffers

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