Variant Analysis Pipeline Research Articles

VUStruct: a compute pipeline for high throughput and personalized structural biology.

Effective diagnosis and treatment of rare genetic disorders requires the interpretation of a patient's genetic variants of unknown significance (VUSs). Today, clinical decision-making is primarily guided by gene-phenotype association databases and DNA-based scoring methods. Our web-accessible variant analysis pipeline, VUStruct, supplements these established approaches by deeply analyzing the downstream molecular impact of variation in context of 3D protein structure. VUStruct's growing impact is fueled by the co-proliferation of protein 3D structural models, gene sequencing, compute power, and artificial intelligence. Contextualizing VUSs in protein 3D structural models also illuminates longitudinal genomics studies and biochemical bench research focused on VUS, and we created VUStruct for clinicians and researchers alike. We now introduce VUStruct to the broad scientific community as a mature, web-facing, extensible, High Performance Computing (HPC) software pipeline. VUStruct maps missense variants onto automatically selected protein structures and launches a broad range of analyses. These include energy-based assessments of protein folding and stability, pathogenicity prediction through spatial clustering analysis, and machine learning (ML) predictors of binding surface disruptions and nearby post-translational modification sites. The pipeline also considers the entire input set of VUS and identifies genes potentially involved in digenic disease. VUStruct's utility in clinical rare disease genome interpretation has been demonstrated through its analysis of over 175 Undiagnosed Disease Network (UDN) Patient cases. VUStruct-leveraged hypotheses have often informed clinicians in their consideration of additional patient testing, and we report here details from two cases where VUStruct was key to their solution. We also note successes with academic research collaborators, for whom VUStruct has informed research directions in both computational genomics and wet lab studies.

Discovering predisposing genes for hereditary breast cancer using deep learning.

Breast cancer (BC) is the most common malignancy affecting Western women today. It is estimated that as many as 10% of BC cases can be attributed to germline variants. However, the genetic basis of the majority of familial BC cases has yet to be identified. Discovering predisposing genes contributing to familial BC is challenging due to their presumed rarity, low penetrance, and complex biological mechanisms. Here, we focused on an analysis of rare missense variants in a cohort of 12 families of Middle Eastern origins characterized by a high incidence of BC cases. We devised a novel, high-throughput, variant analysis pipeline adapted for family studies, which aims to analyze variants at the protein level by employing state-of-the-art machine learning models and three-dimensional protein structural analysis. Using our pipeline, we analyzed 1218 rare missense variants that are shared between affected family members and classified 80 genes as candidate pathogenic. Among these genes, we found significant functional enrichment in peroxisomal and mitochondrial biological pathways which segregated across seven families in the study and covered diverse ethnic groups. We present multiple evidence that peroxisomal and mitochondrial pathways play an important, yet underappreciated, role in both germline BC predisposition and BC survival.

Dissection of the E8 locus in two early maturing Canadian soybean populations.

Soybean [Glycine max (L.) Merr.] is a short-day crop for which breeders want to expand the cultivation range to more northern agro-environments by introgressing alleles involved in early reproductive traits. To do so, we investigated quantitative trait loci (QTL) and expression quantitative trait loci (eQTL) regions comprised within the E8 locus, a large undeciphered region (~7.0 Mbp to 44.5 Mbp) associated with early maturity located on chromosome GM04. We used a combination of two mapping algorithms, (i) inclusive composite interval mapping (ICIM) and (ii) genome-wide composite interval mapping (GCIM), to identify major and minor regions in two soybean populations (QS15524F2:F3 and QS15544RIL) having fixed E1, E2, E3, and E4 alleles. Using this approach, we identified three main QTL regions with high logarithm of the odds (LODs), phenotypic variation explained (PVE), and additive effects for maturity and pod-filling within the E8 region: GM04:16,974,874-17,152,230 (E8-r1); GM04:35,168,111-37,664,017 (E8-r2); and GM04:41,808,599-42,376,237 (E8-r3). Using a five-step variant analysis pipeline, we identified Protein far-red elongated hypocotyl 3 (Glyma.04G124300; E8-r1), E1-like-a (Glyma.04G156400; E8-r2), Light-harvesting chlorophyll-protein complex I subunit A4 (Glyma.04G167900; E8-r3), and Cycling dof factor 3 (Glyma.04G168300; E8-r3) as the most promising candidate genes for these regions. A combinatorial eQTL mapping approach identified significant regulatory interactions for 13 expression traits (e-traits), including Glyma.04G050200 (Early flowering 3/E6 locus), with the E8-r3 region. Four other important QTL regions close to or encompassing major flowering genes were also detected on chromosomes GM07, GM08, and GM16. In GM07:5,256,305-5,404,971, a missense polymorphism was detected in the candidate gene Glyma.07G058200 (Protein suppressor of PHYA-105). These findings demonstrate that the locus known as E8 is regulated by at least three distinct genomic regions, all of which comprise major flowering genes.

Mity: A Highly Sensitive Mitochondrial Variant Analysis Pipeline for Whole Genome Sequencing Data

mity: A Highly Sensitive Mitochondrial Variant Analysis Pipeline for Whole Genome Sequencing Data

2299. Genomic Epidemiology of SARS-CoV-2 in Metropolitan Detroit

Abstract Background The COVID-19 pandemic, resulting from the rapidly evolving SARS-CoV-2 virus, has drastically impacted health systems and economies worldwide. Genomic sequencing is critical for the surveillance of SARS-CoV-2 to monitor the rapidly evolving virus and identify new strains. Methods 583 isolates from Henry Ford Health were retrospectively profiled across 3 years (117 isolates in 2020; 39 in 2021; 427 in 2022). DNA was extracted using Kingfisher viral isolation kit; RT-PCR screening was used to identify isolates with cycle threshold < 32 for whole genome sequencing (WGS). Libraries were generated using QIAseq DIRECT SARS-CoV-2 Kit, followed by Illumina sequencing (MiSeq or NovaSeq 6000; 300 cycles). Lineage analysis of the SARS-CoV-2 consensus genome sequences generated from samtools variant analysis pipeline was determined using Nextclade and Pangolin software. Results Sequences were classified into 11 unique clades across 108 lineages by Nextclade and Pangolin, respectively. Almost three-fourths of the sequenced isolates were from 2022. 117 (20%) genomes were from the early pandemic (2020) and were clustered into 8 clades: 19A, 19B, 20A, 20B, 20C, 21 J (Delta) and 21L (Omicron). Most of the 2022 genomes (72%) were in clade 20C from lineage B.1, identified during the outbreak in Europe. 15 (13%) genomes had clade 19A (lineage B) and 1 had 19B (lineage A.3), identified early in the pandemic in Wuhan, China. Of the 39 (7%) genomes from 2021, the majority (82%) clustered into clade Delta 21J that originated in India. Only 2 (5%) of the genomes from 2021 had Alpha variant of concern (21I) from the lineage B1.1.7, which were suspected to be more transmissible. Of the 427 (73%) genomes from 2022, 393 (92%) had variants within Omicron variant of concern (21L), with 79 different lineages; 1 was Omicron variant 21K from the lineage BA1.1. Other clades observed in the 2022 batch were 19A (6%), 20A (0.2%), 20B (0.2%), 20C (0.2%) and 21M (1%). Conclusion Our genomic surveillance data suggest that SARS-CoV-2 infections at the local level mirrored global outbreaks. This underscores the importance of robust genomic surveillance efforts to inform public health planning and practice. Disclosures Marcus Zervos, MD, Contrafect: Advisor/Consultant|GSK: Grant/Research Support|Johnson and Johnson: Grant/Research Support|Pfizer: Grant/Research Support

Optical genome mapping for cytogenomic analysis of hematological neoplasms.

e19016 Background: Optical genome mapping (OGM) provides a genome-wide analysis for structural variants and copy number changes in one-assay. Compared with conventional G-banded karyotyping (GBK), OGM provides significantly higher resolution and does not require metaphase cells. In this study, we assessed the added value of OGM for cytogenomic analysis of hematological neoplasms. Methods: OGM was performed on blood or bone marrow using Saphyr from Bionano Genomics on 63 patients diagnosed with various types of hematological malignancy (see Table). Variant calling was made by the Rare Variant Analysis pipeline on Bionano Access after applying recommended filters. Each patient had karyotype and/or fluorescence in situ hybridization (FISH) results available for comparison and confirmation. Only Tier 1 (pathogenic) and Tier 2 (likely pathologic) abnormalities were included for comparison purposes in this study. Results: The results were completely concordant between GBK/FISH and OGM in 30 (47.6%) patients, including 10 patients who showed no cytogenetic abnormality (normal). Fully discordant results were found in 8 (12.7%) patients: 2 had an abnormal karyotype but normal OGM, the discrepancy was mainly due to the clonal size (<20%) below the limitation of detection of OGM; 6 had a normal karyotype but cytogenetic abnormalities were detected by OGM, the discrepancy was mainly due to failure of tumor cells growth (n=5) or subtle cytogenetic abnormality undetectable by GBK (n=1). Partially concordant results between karyotype/FISH and OGM were observed in 25 (40%) patients, some abnormalities were detected by both, some were detected by GBK alone (n=4) or by OGM alone (n=25), the latter group included 10 cases with chromothripsis which only be detected by OGM. Overall, OGM provided clinically significant information in 14 (22%) patients regarding diagnosis, risk stratification, and/or identifying therapeutic targets. The added value was more common in lymphoid neoplasms (9 of 27 patients, 33%). Conclusions: OGM provides additional clinically relevant cytogenetic information in the workup of hematological neoplasms. This added value is more apparent in lymphoid neoplasms. These improvements are largely contributable to the higher resolution provided by OGM and analysis independent of metaphase cells. A major limitation of OGM usage is the need for a clonal size of at least 20%. [Table: see text]

Genomic Diagnosis of Rare Pediatric Disease in the United Kingdom and Ireland

BackgroundPediatric disorders include a range of highly penetrant, genetically heterogeneous conditions amenable to genomewide diagnostic approaches. Finding a molecular diagnosis is challenging but can have profound lifelong benefits.MethodsWe conducted a large-scale sequencing study involving more than 13,500 families with probands with severe, probably monogenic, difficult-to-diagnose developmental disorders from 24 regional genetics services in the United Kingdom and Ireland. Standardized phenotypic data were collected, and exome sequencing and microarray analyses were performed to investigate novel genetic causes. We developed an iterative variant analysis pipeline and reported candidate variants to clinical teams for validation and diagnostic interpretation to inform communication with families. Multiple regression analyses were performed to evaluate factors affecting the probability of diagnosis.ResultsA total of 13,449 probands were included in the analyses. On average, we reported 1.0 candidate variant per parent–offspring trio and 2.5 variants per singleton proband. Using clinical and computational approaches to variant classification, we made a diagnosis in approximately 41% of probands (5502 of 13,449). Of 3599 probands in trios who received a diagnosis by clinical assertion, approximately 76% had a pathogenic de novo variant. Another 22% of probands (2997 of 13,449) had variants of uncertain significance in genes that were strongly linked to monogenic developmental disorders. Recruitment in a parent–offspring trio had the largest effect on the probability of diagnosis (odds ratio, 4.70; 95% confidence interval [CI], 4.16 to 5.31). Probands were less likely to receive a diagnosis if they were born extremely prematurely (i.e., 22 to 27 weeks’ gestation; odds ratio, 0.39; 95% CI, 0.22 to 0.68), had in utero exposure to antiepileptic medications (odds ratio, 0.44; 95% CI, 0.29 to 0.67), had mothers with diabetes (odds ratio, 0.52; 95% CI, 0.41 to 0.67), or were of African ancestry (odds ratio, 0.51; 95% CI, 0.31 to 0.78).ConclusionsAmong probands with severe, probably monogenic, difficult-to-diagnose developmental disorders, multimodal analysis of genomewide data had good diagnostic power, even after previous attempts at diagnosis. (Funded by the Health Innovation Challenge Fund and Wellcome Sanger Institute.)

SNPAAMapper-Python: A highly efficient genome-wide SNP variant analysis pipeline for Next-Generation Sequencing data.

Currently, there are many publicly available Next Generation Sequencing tools developed for variant annotation and classification. However, as modern sequencing technology produces more and more sequencing data, a more efficient analysis program is desired, especially for variant analysis. In this study, we updated SNPAAMapper, a variant annotation pipeline by converting perl codes to python for generating annotation output with an improved computational efficiency and updated information for broader applicability. The new pipeline written in Python can classify variants by region (Coding Sequence, Untranslated Regions, upstream, downstream, intron), predict amino acid change type (missense, nonsense, etc.), and prioritize mutation effects (e.g., synonymous > non-synonymous) while being faster and more efficient. Our new pipeline works in five steps. First, exon annotation files are generated. Next, the exon annotation files are processed, and gene mapping and feature information files are produced. Afterward, the python scrips classify the variants based on genomic regions and predict the amino acid change category. Lastly, another python script prioritizes and ranks the mutation effects of variants to output the result file. The Python version of SNPAAMapper accomplished the overall speed by running most annotation steps in a substantially shorter time. The Python script can classify variants by region in 53 s compared to 166 s for the Perl script in a test sample run on a Latitude 7480 Desktop computer with 8GB RAM and an Intel Core i5-6300 CPU @ 2.4Ghz. Steps of predicting amino acid change type and prioritizing mutation effects of variants were executed within 1 s for both pipelines. SNPAAMapper-Python was developed and tested on the ClinVar database, a NCBI database of information on genomic variation and its relationship to human health. We believe our developed Python version of SNPAAMapper variant annotation pipeline will benefit the community by elucidating the variant consequence and speed up the discovery of causative genetic variants through whole genome/exome sequencing. Source codes, test data files, instructions, and further explanations are available on the web at https://github.com/BaiLab/SNPAAMapper-Python.

Whole exome sequencing reveals potentially pathogenic variants in a small subset of premenopausal women with idiopathic osteoporosis.

Osteoporosis in premenopausal women with intact gonadal function and no known secondary cause of bone loss is termed idiopathic osteoporosis (IOP). Women with IOP diagnosed in adulthood have profound bone structural deficits and often report adult and childhood fractures, and family history of osteoporosis. Some have very low bone formation rates (BFR/BS) suggesting osteoblast dysfunction. These features led us to investigate potential genetic etiologies of bone fragility. In 75 IOP women (aged 20-49) with low trauma fractures and/or very low BMD who had undergone transiliac bone biopsies, we performed Whole Exome Sequencing (WES) using our variant analysis pipeline to select candidate rare and novel variants likely to affect known disease genes. We ran rare-variant burden analyses on all genes individually and on phenotypically-relevant gene sets. For particular genes implicated in osteoporosis, we also assessed the frequency of all (including common) variants in subjects versus 6540 non-comorbid female controls. The variant analysis pipeline identified 4 women with 4 heterozygous variants in LRP5 and PLS3 that were considered to contribute to osteoporosis. All 4 women had adult fractures, and 3 women also had multiple fractures, childhood fractures and a family history of osteoporosis. Two women presented during pregnancy/lactation. In an additional 4 subjects, 4 different relevant Variants of Uncertain Significance (VUS) were detected in the genes FKBP10, SLC34A3, and HGD. Of the subjects with VUS, 2 had multiple adult fractures, childhood fractures, and presented during pregnancy/lactation, and 2 had nephrolithiasis. BFR/BS varied among the 8 subjects with identified variants; BFR/BS was quite low in those with variants that are likely to have adverse effects on bone formation. The analysis pipeline did not discover candidate variants in COL1A1, COL1A2, WNT, or ALPL. Although we found several novel and rare variants in LRP5, cases did not have an increased burden of common LRP5 variants compared to controls. Cohort-wide collapsing analysis did not reveal any novel disease genes with genome-wide significance for qualifying variants between controls and our 75 cases. In summary, WES revealed likely pathogenic variants or relevant VUS in 8 (11%) of 75 women with IOP. Notably, the genetic variants identified were consistent with the affected women's diagnostic evaluations that revealed histological evidence of low BFR/BS or biochemical evidence of increased bone resorption and urinary calcium excretion. These results, and the fact that the majority of the women had no identifiable genetic etiology, also suggest that the pathogenesis of and mechanisms leading to osteoporosis in this cohort are heterogeneous. Future research is necessary to identify both new genetic and non-genetic etiologies of early-onset osteoporosis.

Abstract PS16-02: Moving in the fast lane: Test design and validation to produce up-to-date hereditary breast and gynecologic cancer tests

Abstract Introduction Germline genetic testing is increasingly relevant to breast and gynecologic (GYN) cancer clinicians for monitoring and managing high-risk patients. In particular, multi-gene panels (MGPs) can identify unsuspected cancer syndromes and variants that may become clinically significant. Effective MGPs must be comprehensive and up-to-date. For the current study, we used a probe panel designed for targeted enrichment of 4,500 genes associated with various inherited diseases to develop and validate a 66-gene comprehensive hereditary cancer panel, including subsets of genes associated with breast and GYN cancers. Materials and Methods Genomic DNA was extracted and taken through next generation sequencing (NGS) library preparation to be sequenced on an Illumina NovaSeq instrument. Targeted capture-based enrichment with a long-range PCR (LR-PCR) component was used to interrogate all protein-coding exons, intron-exon splice sites (+/-10bp), as well as clinically relevant deep intronic, 5’UTR, and 3’UTR regions for single nucleotide variants (SNVs) and insertions/deletions (indels) of all genes of interest. Copy number variations (CNVs) were also interrogated for all applicable regions. Data analysis was performed using a proprietary in-house bioinformatics variant analysis pipeline. For validation, samples from the Coriell Repository and more than 100 unique de-identified genomic DNA specimens from whole blood and saliva were analyzed for 17,911 variants in 508 genes. Variants were previously identified by orthogonal methods (in-house Sanger sequencing, CLIA validated NGS assays, and microarray). The well-characterized Genome in a Bottle (GIAB) NA12878 and Ashkenazim Trio samples (NA24149, NA24385, and NA24143) were also included. The analytic sensitivity (Positive Percent Agreement, %PPA) and specificity (Technical Positive Predictive Value, %TPPV and Negative Percent Agreement, %NPA) were determined for each variant type (SNV, indel, and CNV). The 66-gene hereditary cancer panel includes genes that confer ≥2-fold increased risk or 5% lifetime risk for developing cancer (APC, ATM, AXIN2, BAP1, BARD1, BLM, BMPR1A, BRCA1, BRCA2, BRIP1, CDH1, CDK4, CDKN1B, CDKN2A (p16, p14), CHEK2, DICER1, EGFR, EPCAM, FANCA, FANCC, FANCM, FH, FLCN, GALNT12, GREM1, HOXB13, MAX, MEN1, MET, MITF, MLH1, MRE11 (MRE11A), MSH2, MSH3, MSH6, MUTYH, NBN, NF1, NTHL1, PALB2, PMS2, POLD1, POLE, POT1, PTCH1, PTEN, RAD50, RAD51C, RAD51D, RECQL, RET, SDHA, SDHAF2, SDHB, SDHC, SDHD, SMARCA4, SMAD4, STK11, SUFU, TMEM127, TP53, TSC1, TSC2, VHL, and XRCC2). Of these, 30 genes increase the lifetime risk of breast and/or GYN cancers and are also distributed among smaller phenotype-specific panels. Results The analytical sensitivity (%PPA) for SNVs and indels was 100.0% and 97.8% for CNVs. The overall specificity for SNVs, indels, and CNVs was >99.0%. The %TPPV for SNVs, Indels, and CNVs was 100.0%, 99.3%, and 100.0%, respectively. The %NPA for SNVs, Indels, and CNVs was 100.0%. The %PPA, %TPPV, and %NPA for LR-PCR was 100.0%. Conclusion Validation of the 66-gene hereditary cancer panel demonstrated high analytical sensitivity and specificity. As additional gene-cancer associations are established, using an already designed and developed comprehensive 4,500 gene panel will expedite the process of updating panel tests to include relevant candidate genes, allowing clinicians and patients to benefit from up-to-date and comprehensive testing. Citation Format: Taraneh E Angeloni, Anindya Bhattacharya, Linda L Cheng, Hansook K Chong, Kelli M Conlan, Christopher D Elzinga, Anna Gerasimova, David Grover, Andrew Grupe, Michael Hua, Domagoj Hodko, Krista Kazmierkiewicz, Rebecca E Nakles, Camille R Nery, Renius Owen, Dana M Goos-Root, Charles M Rowland, Alla Smolgovsky, Elaine C Weltmer, Ke Zhang, Felicitas L Lacbawan. Moving in the fast lane: Test design and validation to produce up-to-date hereditary breast and gynecologic cancer tests [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS16-02.

Systematic evaluation of PAXgene® tissue fixation for the histopathological and molecular study of lung cancer.

Whilst adequate for most existing pathological tests, formalin is generally considered a poor DNA preservative and use of alternative fixatives may prove advantageous for molecular testing of tumour material; an increasingly common approach to identify targetable driver mutations in lung cancer patients. We collected paired PAXgene® tissue‐fixed and formalin‐fixed samples of block‐sized tumour and lung parenchyma, Temno‐needle core tumour biopsies and fine needle tumour aspirates (FNAs) from non‐small cell lung cancer resection specimens. Traditionally processed formalin fixed paraffin wax embedded (FFPE) samples were compared to paired PAXgene® tissue fixed paraffin‐embedded (PFPE) samples. We evaluated suitability for common laboratory tests (H&E staining and immunohistochemistry) and performance for downstream molecular investigations relevant to lung cancer, including RT‐PCR and next generation DNA sequencing (NGS). Adequate and comparable H&E staining was seen in all sample types and nuclear staining was preferable in PAXgene® fixed Temno tumour biopsies and tumour FNA samples. Immunohistochemical staining was broadly comparable. PFPE samples enabled greater yields of less‐fragmented DNA than FFPE comparators. PFPE samples were also superior for PCR and NGS performance, both in terms of quality control metrics and for variant calling. Critically we identified a greater number of genetic variants in the epidermal growth factor receptor gene when using PFPE samples and the Ingenuity® Variant Analysis pipeline. In summary, PFPE samples are adequate for histopathological diagnosis and suitable for the majority of existing laboratory tests. PAXgene® fixation is superior for DNA and RNA integrity, particularly in low‐yield samples and facilitates improved NGS performance, including the detection of actionable lung cancer mutations for precision medicine in lung cancer samples.

Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data.

The wealth of information deliverable from transcriptome sequencing (RNA-seq) is significant, however current applications for variant detection still remain a challenge due to the complexity of the transcriptome. Given the ability of RNA-seq to reveal active regions of the genome, detection of RNA-seq SNPs can prove valuable in understanding the phenotypic diversity between populations. Thus, we present a novel computational workflow named VAP (Variant Analysis Pipeline) that takes advantage of multiple RNA-seq splice aware aligners to call SNPs in non-human models using RNA-seq data only. We applied VAP to RNA-seq from a highly inbred chicken line and achieved high accuracy when compared with the matching whole genome sequencing (WGS) data. Over 65% of WGS coding variants were identified from RNA-seq. Further, our results discovered SNPs resulting from post transcriptional modifications, such as RNA editing, which may reveal potentially functional variation that would have otherwise been missed in genomic data. Even with the limitation in detecting variants in expressed regions only, our method proves to be a reliable alternative for SNP identification using RNA-seq data. The source code and user manuals are available at https://modupeore.github.io/VAP/.

A comprehensive map of single-base polymorphisms in the hypervariable LPA kringle IV type 2 copy number variation region

Lipoprotein (a) [Lp(a)] concentrations are among the strongest genetic risk factors for cardiovascular disease and present pronounced interethnic and interindividual differences. Approximately 90% of Lp(a) variance is controlled by the LPA gene, which contains a 5.6-kb-large copy number variation [kringle IV type 2 (KIV-2) repeat] that generates >40 protein isoforms. Variants within the KIV-2 region are not called in common sequencing projects, leaving up to 70% of the LPA coding region currently unaddressed. To completely assess the variability in LPA, we developed a sequencing strategy for this region and report here the first map of genetic variation in the KIV-2 region, a comprehensively evaluated ultradeep sequencing protocol, and an easy-to-use variant analysis pipeline. We sequenced 123 Central-European individuals and reanalyzed public data of 2,504 individuals from 26 populations. We found 14 different loss-of-function and splice-site mutations, as well as >100, partially even common, missense variants. Some coding variants were frequent in one population but absent in others. This provides novel candidates to explain the large ethnic and individual differences in Lp(a) concentrations. Importantly, our approach and pipeline are also applicable to other similar copy number variable regions, allowing access to regions that are not captured by common genome sequencing.

The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses.

GeneCards, the human gene compendium, enables researchers to effectively navigate and inter-relate the wide universe of human genes, diseases, variants, proteins, cells, and biological pathways. Our recently launched Version 4 has a revamped infrastructure facilitating faster data updates, better-targeted data queries, and friendlier user experience. It also provides a stronger foundation for the GeneCards suite of companion databases and analysis tools. Improved data unification includes gene-disease links via MalaCards and merged biological pathways via PathCards, as well as drug information and proteome expression. VarElect, another suite member, is a phenotype prioritizer for next-generation sequencing, leveraging the GeneCards and MalaCards knowledgebase. It automatically infers direct and indirect scored associations between hundreds or even thousands of variant-containing genes and disease phenotype terms. VarElect's capabilities, either independently or within TGex, our comprehensive variant analysis pipeline, help prepare for the challenge of clinical projects that involve thousands of exome/genome NGS analyses. © 2016 by John Wiley & Sons, Inc.

OP29NEXT GENERATION SEQUENCING : A COMPREHENSIVE GENETIC TEST TO PROVIDE DIAGNOSTIC, PROGNOSTIC AND PREDICTIVE INFORMATION IN BRAIN TUMOUR SAMPLES

INTRODUCTION: There are currently a wide range of different diagnostic genetic tests for gliomas including IDH1/2 testing and loss of heterozygosity (LOH) 1p and 19q. These tests aid diagnosis, classification and can inform therapy choices. Next Generation Sequencing (NGS) can be used to target multiple regions of the genome simultaneously and the diagnostic utility for NGS is well established in our laboratory for germline mutations. We carried out a proof of principle study to investigate the utility of this technology on FFPE glioma samples, with a view to combine existing genetic tests and expand the number of genes investigated. This panel approach will be key for future clinical trials and treatment with personalised therapies. METHOD: We designed a TruSeq Custom Amplicon NGS panel for the detection of mutations in 25 genes known to be mutated in gliomas or other solid tumours. SNPs were also targeted for LOH analysis. We tested 5 non-glioma solid tumour samples and 5 glioma samples with known genetic changes and 6 glioma samples with unknown genetics on the panel. RESULTS: All known mutations were successfully detected by this NGS panel. We were also able to detect LOH. We identified additional clinically significant mutations in genes including ATRX, BRAF, CIC, FUBP1, KRAS, PIK3CA and TP53. CONCLUSION: We have proved in principle that NGS can be successfully used to detect point mutations and LOH in FFPE glioma samples. Future work will improve the variant analysis pipeline, analyse gene copy number changes and detect gene fusion events.

Utility and limitations of exome sequencing as a genetic diagnostic tool for conditions associated with pediatric sudden cardiac arrest/sudden cardiac death.

BackgroundConditions associated with sudden cardiac arrest/death (SCA/D) in youth often have a genetic etiology. While SCA/D is uncommon, a pro-active family screening approach may identify these inherited structural and electrical abnormalities prior to symptomatic events and allow appropriate surveillance and treatment. This study investigated the diagnostic utility of exome sequencing (ES) by evaluating the capture and coverage of genes related to SCA/D.MethodsSamples from 102 individuals (13 with known molecular etiologies for SCA/D, 30 individuals without known molecular etiologies for SCA/D and 59 with other conditions) were analyzed following exome capture and sequencing at an average read depth of 100X. Reads were mapped to human genome GRCh37 using Novoalign, and post-processing and analysis was done using Picard and GATK. A total of 103 genes (2,190 exons) related to SCA/D were used as a primary filter. An additional 100 random variants within the targeted genes associated with SCA/D were also selected and evaluated for depth of sequencing and coverage. Although the primary objective was to evaluate the adequacy of depth of sequencing and coverage of targeted SCA/D genes and not for primary diagnosis, all patients who had SCA/D (known or unknown molecular etiologies) were evaluated with the project’s variant analysis pipeline to determine if the molecular etiologies could be successfully identified.ResultsThe majority of exons (97.6 %) were captured and fully covered on average at minimum of 20x sequencing depth. The proportion of unique genomic positions reported within poorly covered exons remained small (4 %). Exonic regions with less coverage reflect the need to enrich these areas to improve coverage. Despite limitations in coverage, we identified 100 % of cases with a prior known molecular etiology for SCA/D, and analysis of an additional 30 individuals with SCA/D but no known molecular etiology revealed a diagnostic answer in 5/30 (17 %). We also demonstrated 95 % of 100 randomly selected reported variants within our targeted genes would have been picked up on ES based on our coverage analysis.ConclusionsES is a helpful clinical diagnostic tool for SCA/D given its potential to successfully identify a molecular diagnosis, but clinicians should be aware of limitations of available platforms from technical and diagnostic perspectives.Electronic supplementary materialThe online version of this article (doi:10.1186/s40246-015-0038-y) contains supplementary material, which is available to authorized users.

Open pipelines for integrated tumor genome profiles reveal differences between pancreatic cancer tumors and cell lines.

We describe open, reproducible pipelines that create an integrated genomic profile of a cancer and use the profile to find mutations associated with disease and potentially useful drugs. These pipelines analyze high-throughput cancer exome and transcriptome sequence data together with public databases to find relevant mutations and drugs. The three pipelines that we have developed are: (1) an exome analysis pipeline, which uses whole or targeted tumor exome sequence data to produce a list of putative variants (no matched normal data are needed); (2) a transcriptome analysis pipeline that processes whole tumor transcriptome sequence (RNA-seq) data to compute gene expression and find potential gene fusions; and (3) an integrated variant analysis pipeline that uses the tumor variants from the exome pipeline and tumor gene expression from the transcriptome pipeline to identify deleterious and druggable mutations in all genes and in highly expressed genes. These pipelines are integrated into the popular Web platform Galaxy at http://usegalaxy.org/cancer to make them accessible and reproducible, thereby providing an approach for doing standardized, distributed analyses in clinical studies. We have used our pipeline to identify similarities and differences between pancreatic adenocarcinoma cancer cell lines and primary tumors.

Rare Variant Testing of Imputed Data: An Analysis Pipeline Typified

Important methodological advancements in rare variant association testing have been made recently, among them collapsing tests, kernel methods and the variable threshold (VT) technique. Typically, rare variants from a region of interest are tested for association as a group (‘bin'). Rare variant studies are already routinely performed as whole-exome sequencing studies. As an alternative approach, we propose a pipeline for rare variant analysis of imputed data and develop respective quality control criteria. We provide suggestions for the choice and construction of analysis bins in whole-genome application and support the analysis with implementations of standard burden tests (COLL, CMAT) in our INTERSNP-RARE software. In addition, three rare variant regression tests (REG, FRACREG and COLLREG) are implemented. All tests are accompanied with the VT approach which optimizes the definition of ‘rareness'. We integrate kernel tests as implemented in SKAT/SKAT-O into the suggested strategies. Then, we apply our analysis scheme to a genome-wide association study of Alzheimer's disease. Further, we show that our pipeline leads to valid significance testing procedures with controlled type I error rates. Strong association signals surrounding the known APOE locus demonstrate statistical power. In addition, we highlight several suggestive rare variant association findings for follow-up studies, including genomic regions overlapping MCPH1, MED18 and NOTCH3. In summary, we describe and support a straightforward and cost-efficient rare variant analysis pipeline for imputed data and demonstrate its feasibility and validity. The strategy can complement rare variant studies with next generation sequencing data.

English

Many NGS analysis tools focusing on read alignment and variant calling functions for exome sequencing data have been developed in recent years. However, publicly available tools dealing with the downstream analysis of genome-wide variants are fewer and have limited functionality. We developed SNPAAMapper, a novel variant analysis pipeline that can effectively classify variants by region (e.g. CDS, UTRs, intron, upstream, downstream), predict amino acid change type (e.g. synonymous, non-synonymous mutation), and prioritize mutation effects (e.g. CDS versus UTRs). Additional functionality afforded by our pipeline includes: checking variants at exon/intron junctions, customized homozygosity and allele frequency cutoff parameters, and annotation of known variants with dbSNP information, listing original and mutated amino acid sequences containing variants. The final result is reported in a spreadsheet format table containing all variant associated information and prioritized amino acids effects for investigators to examine.AvailabilityPerl scripts and required input files are available on the web at http://www.ccmb.med.umich.edu/ccdu /SNPAAMapper.

An integrative variant analysis pipeline for accurate genotype/haplotype inference in population NGS data

Next-generation sequencing is a powerful approach for discovering genetic variation. Sensitive variant calling and haplotype inference from population sequencing data remain challenging. We describe methods for high-quality discovery, genotyping, and phasing of SNPs for low-coverage (approximately 5×) sequencing of populations, implemented in a pipeline called SNPTools. Our pipeline contains several innovations that specifically address challenges caused by low-coverage population sequencing: (1) effective base depth (EBD), a nonparametric statistic that enables more accurate statistical modeling of sequencing data; (2) variance ratio scoring, a variance-based statistic that discovers polymorphic loci with high sensitivity and specificity; and (3) BAM-specific binomial mixture modeling (BBMM), a clustering algorithm that generates robust genotype likelihoods from heterogeneous sequencing data. Last, we develop an imputation engine that refines raw genotype likelihoods to produce high-quality phased genotypes/haplotypes. Designed for large population studies, SNPTools' input/output (I/O) and storage aware design leads to improved computing performance on large sequencing data sets. We apply SNPTools to the International 1000 Genomes Project (1000G) Phase 1 low-coverage data set and obtain genotyping accuracy comparable to that of SNP microarray.