Quality control and integration of genotypes from two calling pipelines for whole genome sequence data in the Alzheimer's disease sequencing project

Naj, Adam C.; Lin, Honghuang; Vardarajan, Badri N.; White, Simon; Lancour, Daniel; Ma, Yiyi; Schmidt, Michael A.; Sun, Fangui; Butkiewicz, Mariusz; Bush, William S.; Kunkle, Brian W.; Malamon, John; Amin, Najaf; Choi, Seung Hoan; Hamilton‐Nelson, Kara L.; Lee, Sven J. van der; Gupta, Namrata; Koboldt, Daniel C.; Saad, Mohamad; Wang, Bowen; Nato, Alejandro Q.; Sohi, Harkirat; Kuzma, Amanda B.; Wang, Li San; Cupples, L. Adrienne; Duijn, Cornelia M. van; Seshadri, Sudha; Schellenberg, Gerard D.; Boerwinkle, Eric; Bis, Joshua C.; Dupuis, Josée; Salerno, William; Wijsman, Ellen M.; Martin, Eden R.; DeStefano, Anita L.

doi:10.1016/j.ygeno.2018.05.004

Cited by 28 publications

(51 citation statements)

References 29 publications

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“…Several WES QC pipelines have been described 9,10 , which use the Genome Analysis Tool Kit (GATK) Variant Quality Score Recalibration (VQSR) approach as their backbones while enhancing GATK’s output by utilizing various hard filters to further screen data based on specific QC metrics. However, no objectively evaluated WGS QC pipeline had been developed until very recently 11 , and this pipeline did not utilize duplicate samples in determining QC filter thresholds or to prioritize filters based on efficacy, and it only considered biallelic variants. WGS studies typically use at least one hard filter based on output parameters from variant calling, but the exact filters and threshold values employed are often arbitrary or not empirically determined 12–14 .…”

Section: Introductionmentioning

confidence: 99%

“…WGS studies typically use at least one hard filter based on output parameters from variant calling, but the exact filters and threshold values employed are often arbitrary or not empirically determined 12–14 . In previous studies, multiallelic (non-biallelic) variants were systematically removed in QC steps prior to downstream analysis 11,15,16 , as they were broadly deemed low in quality. However, as sample sizes in sequencing studies increase 10,17,18 , the prevalence of multiallelic variants rises 19 .…”

Section: Introductionmentioning

confidence: 99%

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Empirical design of a variant quality control pipeline for whole genome sequencing data using replicate discordance

Adelson

Renton

et al. 2019

Sci Rep

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The success of next-generation sequencing depends on the accuracy of variant calls. Few objective protocols exist for QC following variant calling from whole genome sequencing (WGS) data. After applying QC filtering based on Genome Analysis Tool Kit (GATK) best practices, we used genotype discordance of eight samples that were sequenced twice each to evaluate the proportion of potentially inaccurate variant calls. We designed a QC pipeline involving hard filters to improve replicate genotype concordance, which indicates improved accuracy of genotype calls. Our pipeline analyzes the efficacy of each filtering step. We initially applied this strategy to well-characterized variants from the ClinVar database, and subsequently to the full WGS dataset. The genome-wide biallelic pipeline removed 82.11% of discordant and 14.89% of concordant genotypes, and improved the concordance rate from 98.53% to 99.69%. The variant-level read depth filter most improved the genome-wide biallelic concordance rate. We also adapted this pipeline for triallelic sites, given the increasing proportion of multiallelic sites as sample sizes increase. For triallelic sites containing only SNVs, the concordance rate improved from 97.68% to 99.80%. Our QC pipeline removes many potentially false positive calls that pass in GATK, and may inform future WGS studies prior to variant effect analysis.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Empirical design of a variant quality control pipeline for whole genome sequencing data using replicate discordance

Adelson

Renton

et al. 2019

Sci Rep

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“…As a proof of principle, we applied our method to the Alzheimer’s Disease Sequencing Project (ADSP) case-control data [49] to approximate the number of potential mutations our approach could rescue. The ADSP is a large sequencing project organized, in part, to identify functional mutations that influence Alzheimer’s disease development.…”

Section: Resultsmentioning

confidence: 99%

“…Median genome-wide read depths ranged from 35.4x to 42.9x, with a median of 39.4x. Samples were prepared and sequenced as part of the ADSP [49]. These samples were aligned using BWA (vO.5.9).…”

Section: Methodsmentioning

confidence: 99%

Systematic analysis of dark and camouflaged genes: disease-relevant genes hiding in plain sight

Ebbert

Jensen

Jansen-West

et al. 2019

Preprint

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Background The human genome contains ‘dark’ gene regions that cannot be adequately assembled or aligned using standard short-read sequencing technologies, preventing researchers from identifying mutations within these gene regions that may be relevant to human disease. Here, we identify regions that are ‘dark by depth’ (few mappable reads) and others that are ‘camouflaged’ (ambiguous alignment), and we assess how well long-read technologies resolve these regions. We further present an algorithm to resolve most camouflaged regions (including in short-read data) and apply it to the Alzheimer’s Disease Sequencing Project (ADSP; 13142 samples), as a proof of principle. Results Based on standard whole-genome lllumina sequencing data, we identified 37873 dark regions in 5857 gene bodies (3635 protein-coding) from pathways important to human health, development, and reproduction. Of the 5857 gene bodies, 494 (8.4%) were 100% dark (142 protein-coding) and 2046 (34.9%) were ≥5% dark (628 protein-coding). Exactly 2757 dark regions were in protein-coding exons (CDS) across 744 genes. Long-read sequencing technologies from 10x Genomics, PacBio, and Oxford Nanopore Technologies reduced dark CDS regions to approximately 45.1%, 33.3%, and 18.2% respectively. Applying our algorithm to the ADSP, we rescued 4622 exonic variants from 501 camouflaged genes, including a rare, ten-nucleotide frameshift deletion in CR1, a top Alzheimer’s disease gene, found in only five ADSP cases and zero controls. Conclusions While we could not formally assess the CR1 frameshift mutation in Alzheimer’s disease (insufficient sample-size), we believe it merits investigating in a larger cohort. There remain thousands of potentially important genomic regions overlooked by short-read sequencing that are largely resolved by long-read technologies.

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“…The majority of rare variant detection studies utilise, at least in part, WES. Assembly of WES data within an experiment is now relatively standardised, with specifically designed software, quality control and analysis pipelines [74]. However, as evidenced from meta-analysis of GWAS [7,75], much of the power of genetic analysis of complex traits is gained through combination of data from multiple independent experiments.…”

Section: Challenges Of Rare Variant Identificationmentioning

confidence: 99%

Benefits and Challenges of Rare Genetic Variation in Alzheimer’s Disease

2019

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Purpose of Review It is well established that sporadic Alzheimer's disease (AD) is polygenic with common and rare genetic variation alongside environmental factors contributing to disease. Here, we review our current understanding of the genetic architecture of disease, paying specific attention to rare susceptibility variants, and explore some of the limitations in rare variant detection and analysis. Recent Findings Rare variation has been shown to robustly associate with disease. These include potentially damaging and loss of function mutations that are easily modelled in silico, in vitro and in vivo, and represent potentially druggable targets. A number of risk genes, including TREM2, SORL1 and ABCA7 show multiple independent associations suggesting that they may influence disease via multiple mechanisms. With transcriptional regulation, inflammatory response and modification of protein production suggested to be of primary importance. Summary We are at the beginning of our journey of rare variant detection in AD. Whole exome sequencing has been the predominant technology of choice. While fruitful, this has introduced a number of challenges with regard to data integration. Ultimately the future of disease-associated rare variant identification lies in whole genome sequencing projects that will allow the testing of the full range of genomic variation.

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Quality control and integration of genotypes from two calling pipelines for whole genome sequence data in the Alzheimer's disease sequencing project

Cited by 28 publications

References 29 publications

Empirical design of a variant quality control pipeline for whole genome sequencing data using replicate discordance

Empirical design of a variant quality control pipeline for whole genome sequencing data using replicate discordance

Systematic analysis of dark and camouflaged genes: disease-relevant genes hiding in plain sight

Benefits and Challenges of Rare Genetic Variation in Alzheimer’s Disease

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