High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios.

The 1000 Genomes Project (1kGP) is the largest fully open resource of whole-genome sequencing (WGS) data consented for public distribution without access or use restrictions. The final, phase 3 release of the 1kGP included 2,504 unrelated samples from 26 populations and was based primarily on low-coverage WGS. Here, we present a high-coverage 3,202-sample WGS 1kGP resource, which now includes 602 complete trios, sequenced to a depth of 30X using Illumina. We performed single-nucleotide variant (SNV) and short insertion and deletion (INDEL) discovery and generated a comprehensive set of structural variants (SVs) by integrating multiple analytic methods through a machine learning model. We show gains in sensitivity and precision of variant calls compared to phase 3, especially among rare SNVs as well as INDELs and SVs spanning frequency spectrum. We also generated an improved reference imputation panel, making variants discovered here accessible for association studies.

A draft human pangenome reference.

Here the Human Pangenome Reference Consortium presents a first draft of the human pangenome reference. The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individuals1. These assemblies cover more than 99% of the expected sequence in each genome and are more than 99% accurate at the structural and base pair levels. Based on alignments of the assemblies, we generate a draft pangenome that captures known variants and haplotypes and reveals new alleles at structurally complex loci. We also add 119 million base pairs of euchromatic polymorphic sequences and 1,115 gene duplications relative to the existing reference GRCh38. Roughly 90 million of the additional base pairs are derived from structural variation. Using our draft pangenome to analyse short-read data reduced small variant discovery errors by 34% and increased the number of structural variants detected per haplotype by 104% compared with GRCh38-based workflows, which enabled the typing of the vast majority of structural variant alleles per sample.

Mapping and characterization of structural variation in 17,795 human genomes.

A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline1 to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0-11.2% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing.

Author Correction: Exome sequencing of Finnish isolates enhances rare-variant association power.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

Exome sequencing of Finnish isolates enhances rare-variant association power.

Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power.

Association of structural variation with cardiometabolic traits in Finns.

The contribution of genome structural variation (SV) to quantitative traits associated with cardiometabolic diseases remains largely unknown. Here, we present the results of a study examining genetic association between SVs and cardiometabolic traits in the Finnish population. We used sensitive methods to identify and genotype 129,166 high-confidence SVs from deep whole-genome sequencing (WGS) data of 4,848 individuals. We tested the 64,572 common and low-frequency SVs for association with 116 quantitative traits and tested candidate associations using exome sequencing and array genotype data from an additional 15,205 individuals. We discovered 31 genome-wide significant associations at 15 loci, including 2 loci at which SVs have strong phenotypic effects: (1) a deletion of the ALB promoter that is greatly enriched in the Finnish population and causes decreased serum albumin level in carriers (p = 1.47 × 10-54) and is also associated with increased levels of total cholesterol (p = 1.22 × 10-28) and 14 additional cholesterol-related traits, and (2) a multi-allelic copy number variant (CNV) at PDPR that is strongly associated with pyruvate (p = 4.81 × 10-21) and alanine (p = 6.14 × 10-12) levels and resides within a structurally complex genomic region that has accumulated many rearrangements over evolutionary time. We also confirmed six previously reported associations, including five led by stronger signals in single nucleotide variants (SNVs) and one linking recurrent HP gene deletion and cholesterol levels (p = 6.24 × 10-10), which was also found to be strongly associated with increased glycoprotein level (p = 3.53 × 10-35). Our study confirms that integrating SVs in trait-mapping studies will expand our knowledge of genetic factors underlying disease risk.

Mitochondrial genome copy number measured by DNA sequencing in human blood is strongly associated with metabolic traits via cell-type composition differences.

BACKGROUND: Mitochondrial genome copy number (MT-CN) varies among humans and across tissues and is highly heritable, but its causes and consequences are not well understood. When measured by bulk DNA sequencing in blood, MT-CN may reflect a combination of the number of mitochondria per cell and cell-type composition. Here, we studied MT-CN variation in blood-derived DNA from 19184 Finnish individuals using a combination of genome (N = 4163) and exome sequencing (N = 19034) data as well as imputed genotypes (N = 17718). RESULTS: We identified two loci significantly associated with MT-CN variation: a common variant at the MYB-HBS1L locus (P = 1.6 × 10-8), which has previously been associated with numerous hematological parameters; and a burden of rare variants in the TMBIM1 gene (P = 3.0 × 10-8), which has been reported to protect against non-alcoholic fatty liver disease. We also found that MT-CN is strongly associated with insulin levels (P = 2.0 × 10-21) and other metabolic syndrome (metS)-related traits. Using a Mendelian randomization framework, we show evidence that MT-CN measured in blood is causally related to insulin levels. We then applied an MT-CN polygenic risk score (PRS) derived from Finnish data to the UK Biobank, where the association between the PRS and metS traits was replicated. Adjusting for cell counts largely eliminated these signals, suggesting that MT-CN affects metS via cell-type composition. CONCLUSION: These results suggest that measurements of MT-CN in blood-derived DNA partially reflect differences in cell-type composition and that these differences are causally linked to insulin and related traits.

Mutation Clearance after Transplantation for Myelodysplastic Syndrome.

BACKGROUND: Allogeneic hematopoietic stem-cell transplantation is the only curative treatment for patients with myelodysplastic syndrome (MDS). The molecular predictors of disease progression after transplantation are unclear. METHODS: We sequenced bone marrow and skin samples from 90 adults with MDS who underwent allogeneic hematopoietic stem-cell transplantation after a myeloablative or reduced-intensity conditioning regimen. We detected mutations before transplantation using enhanced exome sequencing, and we evaluated mutation clearance by using error-corrected sequencing to genotype mutations in bone marrow samples obtained 30 days after transplantation. In this exploratory study, we evaluated the association of a mutation detected after transplantation with disease progression and survival. RESULTS: Sequencing identified at least one validated somatic mutation before transplantation in 86 of 90 patients (96%); 32 of these patients (37%) had at least one mutation with a maximum variant allele frequency of at least 0.5% (equivalent to 1 heterozygous mutant cell in 100 cells) 30 days after transplantation. Patients with disease progression had mutations with a higher maximum variant allele frequency at 30 days than those who did not (median maximum variant allele frequency, 0.9% vs. 0%; P<0.001). The presence of at least one mutation with a variant allele frequency of at least 0.5% at day 30 was associated with a higher risk of progression (53.1% vs. 13.0%; conditioning regimen-adjusted hazard ratio, 3.86; 95% confidence interval [CI], 1.96 to 7.62; P<0.001) and a lower 1-year rate of progression-free survival than the absence of such a mutation (31.3% vs. 59.3%; conditioning regimen-adjusted hazard ratio for progression or death, 2.22; 95% CI, 1.32 to 3.73; P=0.005). The rate of progression-free survival was lower among patients who had received a reduced-intensity conditioning regimen and had at least one persistent mutation with a variant allele frequency of at least 0.5% at day 30 than among patients with other combinations of conditioning regimen and mutation status (P≤0.001). Multivariate analysis confirmed that patients who had a mutation with a variant allele frequency of at least 0.5% detected at day 30 had a higher risk of progression (hazard ratio, 4.48; 95% CI, 2.21 to 9.08; P<0.001) and a lower 1-year rate of progression-free survival than those who did not (hazard ratio for progression or death, 2.39; 95% CI, 1.40 to 4.09; P=0.002). CONCLUSIONS: The risk of disease progression was higher among patients with MDS in whom persistent disease-associated mutations were detected in the bone marrow 30 days after transplantation than among those in whom these mutations were not detected. (Funded by the Leukemia and Lymphoma Society and others.).

svtools: population-scale analysis of structural variation.

SUMMARY: Large-scale human genetics studies are now employing whole genome sequencing with the goal of conducting comprehensive trait mapping analyses of all forms of genome variation. However, methods for structural variation (SV) analysis have lagged far behind those for smaller scale variants, and there is an urgent need to develop more efficient tools that scale to the size of human populations. Here, we present a fast and highly scalable software toolkit (svtools) and cloud-based pipeline for assembling high quality SV maps-including deletions, duplications, mobile element insertions, inversions and other rearrangements-in many thousands of human genomes. We show that this pipeline achieves similar variant detection performance to established per-sample methods (e.g. LUMPY), while providing fast and affordable joint analysis at the scale of ≥100 000 genomes. These tools will help enable the next generation of human genetics studies. AVAILABILITY AND IMPLEMENTATION: svtools is implemented in Python and freely available (MIT) from https://github.com/hall-lab/svtools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genomic heterogeneity of ALK fusion breakpoints in non-small-cell lung cancer.

In lung adenocarcinoma, canonical EML4-ALK inversion results in a fusion protein with a constitutively active ALK kinase domain. Evidence of ALK rearrangement occurs in a minority (2-7%) of lung adenocarcinoma, and only ~60% of these patients will respond to targeted ALK inhibition by drugs such as crizotinib and ceritinib. Clinically, targeted anti-ALK therapy is often initiated based on evidence of an ALK genomic rearrangement detected by fluorescence in situ hybridization (FISH) of interphase cells in formalin-fixed, paraffin-embedded tissue sections. At the genomic level, however, ALK rearrangements are heterogeneous, with multiple potential breakpoints in EML4, and alternate fusion partners. Using next-generation sequencing of DNA and RNA together with ALK immunohistochemistry, we comprehensively characterized genomic breakpoints in 33 FISH-positive lung adenocarcinomas. Of these 33 cases, 29 (88%) had detectable DNA level ALK rearrangements involving EML4, KIF5B, or non-canonical partners including ASXL2, ATP6V1B1, PRKAR1A, and SPDYA. A subset of 12 cases had material available for RNA-Seq. Of these, eight of eight (100%) cases with DNA rearrangements showed ALK fusion transcripts from RNA-Seq; three of four cases (75%) without detectable DNA rearrangements were similarly negative by RNA-Seq, and one case was positive by RNA-Seq but negative by DNA next-generation sequencing. By immunohistochemistry, 17 of 19 (89%) tested cases were clearly positive for ALK protein expression; the remaining cases had no detectable DNA level rearrangement or had a non-canonical rearrangement not predicted to form a fusion protein. Survival analysis of patients treated with targeted ALK inhibitors demonstrates a significant difference in mean survival between patients with next-generation sequencing confirmed EML4-ALK rearrangements, and those without (20.6 months vs 5.4 months, P<0.01). Together, these data demonstrate abundant genomic heterogeneity among ALK-rearranged lung adenocarcinoma, which may account for differences in treatment response with targeted ALK inhibitors.

SVScore: an impact prediction tool for structural variation.

Summary: Here we present SVScore, a tool for in silico structural variation (SV) impact prediction. SVScore aggregates per-base single nucleotide polymorphism (SNP) pathogenicity scores across relevant genomic intervals for each SV in a manner that considers variant type, gene features and positional uncertainty. We show that the allele frequency spectrum of high-scoring SVs is strongly skewed toward lower frequencies, suggesting that they are under purifying selection, and that SVScore identifies deleterious variants more effectively than alternative methods. Notably, our results also suggest that duplications are under surprisingly strong selection relative to deletions, and that there are a similar number of strongly pathogenic SVs and SNPs in the human population. Availability and Implementation: SVScore is implemented in Perl and available freely at {{ http://www.github.com/lganel/SVScore }} for use under the MIT license. Contact: ihall@wustl.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Spectrum of mutations in leiomyosarcomas identified by clinical targeted next-generation sequencing.

Recurrent genomic mutations in uterine and non-uterine leiomyosarcomas have not been well established. Using a next generation sequencing (NGS) panel of common cancer-associated genes, 25 leiomyosarcomas arising from multiple sites were examined to explore genetic alterations, including single nucleotide variants (SNV), small insertions/deletions (indels), and copy number alterations (CNA). Sequencing showed 86 non-synonymous, coding region somatic variants within 151 gene targets in 21 cases, with a mean of 4.1 variants per case; 4 cases had no putative mutations in the panel of genes assayed. The most frequently altered genes were TP53 (36%), ATM and ATRX (16%), and EGFR and RB1 (12%). CNA were identified in 85% of cases, with the most frequent copy number losses observed in chromosomes 10 and 13 including PTEN and RB1; the most frequent gains were seen in chromosomes 7 and 17. Our data show that deletions in canonical cancer-related genes are common in leiomyosarcomas. Further, the spectrum of gene mutations observed shows that defects in DNA repair and chromosomal maintenance are central to the biology of leiomyosarcomas, and that activating mutations observed in other common cancer types are rare in leiomyosarcomas.

Selection of models for the analysis of risk-factor trees: leveraging biological knowledge to mine large sets of risk factors with application to microbiome data.

MOTIVATION: Establishment of a statistical association between microbiome features and clinical outcomes is of growing interest because of the potential for yielding insights into biological mechanisms and pathogenesis. Extracting microbiome features that are relevant for a disease is challenging and existing variable selection methods are limited due to large number of risk factor variables from microbiome sequence data and their complex biological structure. RESULTS: We propose a tree-based scanning method, Selection of Models for the Analysis of Risk factor Trees (referred to as SMART-scan), for identifying taxonomic groups that are associated with a disease or trait. SMART-scan is a model selection technique that uses a predefined taxonomy to organize the large pool of possible predictors into optimized groups, and hierarchically searches and determines variable groups for association test. We investigate the statistical properties of SMART-scan through simulations, in comparison to a regular single-variable analysis and three commonly-used variable selection methods, stepwise regression, least absolute shrinkage and selection operator (LASSO) and classification and regression tree (CART). When there are taxonomic group effects in the data, SMART-scan can significantly increase power by using bacterial taxonomic information to split large numbers of variables into groups. Through an application to microbiome data from a vervet monkey diet experiment, we demonstrate that SMART-scan can identify important phenotype-associated taxonomic features missed by single-variable analysis, stepwise regression, LASSO and CART.

Candidate gene resequencing to identify rare, pedigree-specific variants influencing healthy aging phenotypes in the long life family study.

BACKGROUND: The Long Life Family Study (LLFS) is an international study to identify the genetic components of various healthy aging phenotypes. We hypothesized that pedigree-specific rare variants at longevity-associated genes could have a similar functional impact on healthy phenotypes. METHODS: We performed custom hybridization capture sequencing to identify the functional variants in 464 candidate genes for longevity or the major diseases of aging in 615 pedigrees (4,953 individuals) from the LLFS, using a multiplexed, custom hybridization capture. Variants were analyzed individually or as a group across an entire gene for association to aging phenotypes using family based tests. RESULTS: We found significant associations to three genes and nine single variants. Most notably, we found a novel variant significantly associated with exceptional survival in the 3' UTR OBFC1 in 13 individuals from six pedigrees. OBFC1 (chromosome 10) is involved in telomere maintenance, and falls within a linkage peak recently reported from an analysis of telomere length in LLFS families. Two different algorithms for single gene associations identified three genes with an enrichment of variation that was significantly associated with three phenotypes (GSK3B with the Healthy Aging Index, NOTCH1 with diastolic blood pressure and TP53 with serum HDL). CONCLUSIONS: Sequencing analysis of family-based associations for age-related phenotypes can identify rare or novel variants.

Admixture mapping identifies a quantitative trait locus associated with FEV1/FVC in the COPDGene Study.

African Americans are admixed with genetic contributions from European and African ancestral populations. Admixture mapping leverages this information to map genes influencing differential disease risk across populations. We performed admixture and association mapping in 3,300 African American current or former smokers from the COPDGene Study. We analyzed estimated local ancestry and SNP genotype information to identify regions associated with FEV1 /FVC, the ratio of forced expiratory volume in one second to forced vital capacity, measured by spirometry performed after bronchodilator administration. Global African ancestry inversely associated with FEV1 /FVC (P = 0.035). Genome-wide admixture analysis, controlling for age, gender, body mass index, current smoking status, pack-years smoked, and four principal components summarizing the genetic background of African Americans in the COPDGene Study, identified a region on chromosome 12q14.1 associated with FEV1 /FVC (P = 2.1 × 10(-6) ) when regressed on local ancestry. Allelic association in this region of chromosome 12 identified an intronic variant in FAM19A2 (rs348644) as associated with FEV1 /FVC (P = 1.76 × 10(-6) ). By combining admixture and association mapping, a marker on chromosome 12q14.1 was identified as being associated with reduced FEV1 /FVC ratio among African Americans in the COPDGene Study.

Admixture mapping of coronary artery calcification in African Americans from the NHLBI family heart study.

BACKGROUND: Coronary artery calcification (CAC) is an imaging biomarker of coronary atherosclerosis. In European Americans, genome-wide association studies (GWAS) have identified several regions associated with coronary artery disease. However, few large studies have been conducted in African Americans. The largest meta-analysis of CAC in African Americans failed to identify genome-wide significant variants despite being powered to detect effects comparable to effects identified in European Americans. Because CAC is different in prevalence and severity in African Americans and European Americans, admixture mapping is a useful approach to identify loci missed by GWAS. RESULTS: We applied admixture mapping to the African American cohort of the Family Heart Study and identified one genome-wide significant region on chromosome 12 and three potential regions on chromosomes 6, 15, and 19 that are associated with CAC. Follow-up studies using previously reported GWAS meta-analysis data suggest that the regions identified on chromosome 6 and 15 contain variants that are possibly associated with CAC. The associated region on chromosome 6 contains the gene for BMP-6, which is expressed in vascular calcific lesions. CONCLUSIONS: Our results suggest that admixture mapping can be a useful hypothesis-generating tool to identify genomic regions that contribute to complex diseases in genetically admixed populations.

A wide spectrum of EGFR mutations in glioblastoma is detected by a single clinical oncology targeted next-generation sequencing panel.

With the advent of large-scale genomic analysis, the genetic landscape of glioblastoma (GBM) has become more clear, including characteristic genetic alterations in EGFR. In routine clinical practice, genetic alterations in GBMs are identified using several disparate techniques that consume already limited amounts of tissue and add to overall testing costs. In this study, we sought to determine if the full spectrum of EGFR mutations in GBMs could be detected using a single next generation sequencing (NGS) based oncology assay in 34 consecutive cases. Using a battery of informatics tools to identify single nucleotide variants, insertions and deletions, and amplification (including variants EGFRvIII and EGFRvV), twenty-one of the 34 (62%) individuals had at least one alteration in EGFR by sequencing, consistent with published datasets. Mutations detected include several single nucleotide variants, amplification (confirmed by fluorescence in situ hybridization), and the variants EGFRvIII and EGFRvV (confirmed by multiplex ligation-dependent probe amplification). Here we show that a single NGS assay can identify the full spectrum of relevant EGFR mutations. Overall, sequencing based diagnostics have the potential to maximize the amount of genetic information obtained from GBMs and simultaneously reduce the total time, required specimen material, and costs associated with current multimodality studies.

T-cell clonality assessment by next-generation sequencing improves detection sensitivity in mycosis fungoides.

BACKGROUND: T-cell receptor (TCR) clonality assessment is a principal diagnostic test in the management of mycosis fungoides (MF). However, current polymerase chain reaction-based methods may produce ambiguous results, often because of low abundance of clonal T lymphocytes, resulting in weak clonal peaks that cannot be size-resolved by contemporary capillary electrophoresis (CE). OBJECTIVE: We sought to determine if next-generation sequencing (NGS)-based detection has increased sensitivity for T-cell clonality over CE-based detection in MF. METHODS: Clonality was determined by an NGS-based method in which the TCR-γ variable region was polymerase chain reaction amplified and the products sequenced to establish the identity of rearranged variable and joining regions. RESULTS: Of the 35 MF cases tested, 29 (85%) showed a clonal T-cell rearrangement by NGS, compared with 15 (44%) by standard CE detection. Three patients with MF had follow-up testing that showed identical, clonal TCR sequences in subsequent skin biopsy specimens. LIMITATIONS: Clonal T-cell populations have been described in benign conditions; evidence of clonality alone, by any method, is not sufficient for diagnosis. CONCLUSION: TCR clonality assessment by NGS has superior sensitivity compared with CE-based detection. Further, NGS enables tracking of specific clones across multiple time points for more accurate identification of recurrent MF.

Retinoblastoma gene mutations detected by whole exome sequencing of Merkel cell carcinoma.

Merkel cell carcinoma is a highly aggressive cutaneous neuroendocrine tumor that has been associated with Merkel cell polyomavirus in up to 80% of cases. Merkel cell polyomavirus is believed to influence pathogenesis, at least in part, through expression of the large T antigen, which includes a retinoblastoma protein-binding domain. However, there appears to be significant clinical and morphological overlap between polyomavirus-positive and polyomavirus-negative Merkel cell carcinoma cases. Although much of the recent focus of Merkel cell carcinoma pathogenesis has been on polyomavirus, the pathogenesis of polyomavirus-negative cases is still poorly understood. We hypothesized that there are underlying human somatic mutations that unify Merkel cell carcinoma pathogenesis across polyomavirus status, and to investigate we performed whole exome sequencing on five polyomavirus-positive cases and three polyomavirus-negative cases. We found that there were no significant differences in the overall number of single-nucleotide variations, copy number variations, insertion/deletions, and chromosomal rearrangements when comparing polyomavirus-positive to polyomavirus-negative cases. However, we did find that the retinoblastoma pathway genes harbored a high number of mutations in Merkel cell carcinoma. Furthermore, the retinoblastoma gene (RB1) was found to have nonsense truncating protein mutations in all three polyomavirus-negative cases; no such mutations were found in the polyomavirus-positive cases. In all eight cases, the retinoblastoma pathway dysregulation was confirmed by immunohistochemistry. Although polyomavirus-positive Merkel cell carcinoma is believed to undergo retinoblastoma dysregulation through viral large T antigen expression, our findings demonstrate that somatic mutations in polyomavirus-negative Merkel cell carcinoma lead to retinoblastoma dysregulation through an alternative pathway. This novel finding suggests that the retinoblastoma pathway dysregulation leads to an overlapping Merkel cell carcinoma phenotype and that oncogenesis occurs through either a polyomavirus-dependent (viral large T antigen expression) or polyomavirus-independent (host somatic mutation) mechanism.

Copy number variants in clinical next-generation sequencing data can define the relationship between simultaneous tumors in an individual patient.

Targeted next-generation sequencing (NGS) cancer panels have become a popular method for the identification of clinically predictive mutations in cancer. Such methods typically detect single nucleotide variants (SNVs) and small insertions/deletions (indels) in known cancer genes and can provide further information regarding diagnosis in challenging surgical pathology cases, as well as identify therapeutic targets and prognostically significant mutations. However, in addition to SNVs and indels, other mutation classes, including copy number variants (CNVs) and translocations, can be simultaneously detected from targeted NGS data. Here, as proof of methods, we present clinical data which demonstrate that targeted NGS panels can separate synchronous liver tumors based on CNV status, in the absence of distinct SNVs and indels. Such CNV-based analysis can be performed without additional cost using existing targeted cancer panel data and publically available software.