Low-coverage Whole-genome Sequencing Research Articles

Design and validation of a high-density single nucleotide polymorphism array for the Eastern oyster (Crassostrea virginica).

Dense single nucleotide polymorphism (SNP) arrays are essential tools for rapid high-throughput genotyping for many genetic analyses, including genomic selection and high-resolution population genomic assessments. We present a high-density (200 K) SNP array developed for the Eastern oyster (Crassostrea virginica), which is a species of significant aquaculture production and restoration efforts throughout its native range. SNP discovery was performed using low-coverage whole-genome sequencing of 435 F1 oysters from families from 11 founder populations in New Brunswick, Canada. An Affymetrix Axiom Custom array was created with 219,447 SNPs meeting stringent selection criteria and validated by genotyping more than 4,000 oysters across 2 generations. In total, 144,570 SNPs had a call rate >90%, most of which (96%) were polymorphic and were distributed across the Eastern oyster reference genome, with similar levels of genetic diversity observed in both generations. Linkage disequilibrium was low (maximum r2 ∼0.32) and decayed moderately with increasing distance between SNP pairs. Taking advantage of our intergenerational data set, we quantified Mendelian inheritance errors to validate SNP selection. Although most of SNPs exhibited low Mendelian inheritance error rates overall, with 72% of called SNPs having an error rate of <1%, many loci had elevated Mendelian inheritance error rates, potentially indicating the presence of null alleles. This SNP panel provides a necessary tool to enable routine application of genomic approaches, including genomic selection, in C. virginica selective breeding programs. As demand for production increases, this resource will be essential for accelerating production and sustaining the Canadian oyster aquaculture industry.

Simultaneous Ultra-Sensitive Detection of Structural and Single Nucleotide Variants Using Multiplex Droplet Digital PCR in Liquid Biopsies from Children with Medulloblastoma.

Medulloblastoma is a malignant embryonal tumor of the central nervous system (CNS) that mainly affects infants and children. Prognosis is highly variable, and molecular biomarkers for measurable residual disease (MRD) detection are lacking. Analysis of cell-free DNA (cfDNA) in cerebrospinal fluid (CSF) using broad genomic approaches, such as low-coverage whole-genome sequencing, has shown promising prognostic value. However, more sensitive methods are needed for MRD analysis. Here, we show the technical feasibility of capturing medulloblastoma-associated structural variants and point mutations simultaneously in cfDNA using multiplexed droplet digital PCR (ddPCR). Assay sensitivity was assessed with a dilution series of tumor in normal genomic DNA, and the limit of detection was below 100 pg of input DNA for all assays. False positive rates were zero for structural variant assays. Liquid biopsies (CSF and plasma, n = 47) were analyzed from 12 children with medulloblastoma, all with negative CSF cytology. MRD was detected in 75% (9/12) of patients overall. In CSF samples taken before or within 21 days of surgery, MRD was detected in 88% (7/8) of patients with localized disease and in one patient with the metastasized disease. Our results suggest that this approach could expand the utility of ddPCR and complement broader analyses of cfDNA for MRD detection.

Combining genome-wide association study based on low-coverage whole genome sequencing and transcriptome analysis to reveal the key candidate genes affecting meat color in pigs.

Meat color is an attractive trait that influences consumers' purchase decisions at the point of sale. To decipher the genetic basis of meat color traits, we performed a genome-wide association study based on low-coverage whole-genome sequencing. In total, 669 (Pietrain × Duroc) × (Landrace × Yorkshire) pigs were genotyped using low-coverage whole-genome sequencing. Single nucleotide polymorphism (SNP) calling and genotype imputation were performed using the BaseVar + STITCH channel. Six individuals with an average depth of 12.05× whole-genome resequencing were randomly selected to assess the accuracy of imputation. Heritability evaluation and genome-wide association study for meat color traits were conducted. Functional enrichment analysis of the candidate genes from genome-wide association study and integration analysis with our previous transcriptome data were conducted. The imputation accuracy parameters, allele frequency R2 , concordance rate, and dosage R2 were 0.959, 0.952, and 0.933, respectively. The heritability values of a*45 min , b*45 min , L*45 min , C*, and H0 were 0.19, 0.11, 0.06, 0.16, and 0.26, respectively. In total, 3884 significant SNPs and 15 QTL, corresponding to 382 genes, were associated with meat color traits. Functional enrichment analysis revealed that 10 genes were the potential candidates for regulating meat color. Moreover, integration analysis revealed that DMRT2, EFNA5, FGF10, and COL11A2 were the most promising candidates affecting meat color. In summary, this study provides new insights into the molecular basis of meat color traits, and provides a new theoretical basis for the molecular breeding of meat color traits in pigs.

Genome-Wide Analysis of Palindrome Formation with Next-Generation Sequencing (GAPF-Seq) and a Bioinformatics Pipeline for Assessing De Novo Palindromes in Cancer Genomes.

DNA palindromes are a type of chromosomal aberration that appears frequently during tumorigenesis. They are characterized by sequences of nucleotides that are identical to their reverse complements and often arise due to illegitimate repair of DNA double-strand breaks, fusion of telomeres, or stalled replication forks, all of which are common adverse early events in cancer. Here, we describe the protocol for enriching palindromes from genomic DNA sources with low-input DNA amounts and detail a bioinformatics tool for assessing the enrichment and location of de novo palindrome formation from low-coverage whole-genome sequencing data.

Accurate genome-wide genotyping from archival tissue to explore the contribution of common genetic variants to pre-cancer outcomes

PurposeThe contribution of common genetic variants to pre-cancer progression is understudied due to long follow-up time, rarity of poor outcomes and lack of available germline DNA collection. Alternatively, DNA from diagnostic archival tissue is available, but its somatic nature, limited quantity and suboptimal quality would require an accurate cost-effective genome-wide germline genotyping methodology.Experimental designBlood and tissue DNA from 10 individuals were used to benchmark the accuracy of Single Nucleotide Polymorphisms (SNP) genotypes, Polygenic Risk Scores (PRS) or HLA haplotypes using low-coverage whole-genome sequencing (lc-WGS) and genotype imputation. Tissue-derived PRS were further evaluated for 36 breast cancer patients (11.7 years median follow-up time) diagnosed with DCIS and used to model the risk of Breast Cancer Subsequent Events (BCSE).ResultsTissue-derived germline DNA profiling resulted in accurate genotypes at common SNPs (blood correlation r2 > 0.94) and across 22 disease-related polygenic risk scores (PRS, mean correlation r = 0.93). Imputed Class I and II HLA haplotypes were 96.7% and 82.5% concordant with clinical-grade blood HLA haplotypes, respectively. In DCIS patients, tissue-derived PRS was significantly associated with BCSE (HR = 2, 95% CI 1.2–3.8). The top and bottom decile patients had an estimated 28% and 5% chance of BCSE at 10 years, respectively.ConclusionsArchival tissue DNA germline profiling using lc-WGS and imputation, represents a cost and resource-effective alternative in the retrospective design of long-term disease genetic studies. Initial results in breast cancer suggest that common risk variants contribute to pre-cancer progression.

Contribution bias of parental genomes to the hybrid lineages of black Amur bream and topmouth culter revealed by low-coverage whole-genome sequencing

Interspecific hybridization has the potential to increase animal genetic diversity, enable the introgression of advantageous genetic variation, and even create hybrid lineages. Using the low-coverage whole-genome sequencing data of black Amur bream (Megalobrama terminalis [SJ]), topmouth culter (Culter alburnus [QZ]), and their hybrid progeny, we focused on the sequence variation and contribution bias to the genomic composition of the hybrid lineages of SJ♀ × QZ♂ [ZJ] and QZ♀ × SJ♂ [FJ]. Both mash-based genetic distance analysis and variant analysis based on various reference genomes revealed that the hybrid lineages' genomes are more prone to QZ composition. Furthermore, we assess the degree of subgenomic intermixing between parents QZ and SJ in the FJ and ZJ hybrid lineages. In the Jaccard index-zero genomic area, 89.41% of the genomic region in ZJ and 90.23% in FJ are entirely from one of the parental genomes. This genomic area is primarily from QZ, regardless of FJ or ZJ. In FJ, the proportion is 55.70%; in ZJ, the proportion is 54.60%. Using the reference-free variant discovery tool, we identified two InDel markers (Both were SSR-type InDel markers) and one (T/A)-type SNP marker that displayed differences in agarose electrophoresis. Our current findings suggest that parental genomic contribution bias leads to an imbalance in the genomic composition of hybrid lineages. The genomic composition bias is connected to species but not maternal effects, giving insights into the development of vertebrate genomes shortly after hybridization.

Cost-effectively dissecting the genetic architecture of complex wool traits in rabbits by low-coverage sequencing

BackgroundRabbit wool traits are important in fiber production and for model organism research on hair growth, but their genetic architecture remains obscure. In this study, we focused on wool characteristics in Angora rabbits, a breed well-known for the quality of its wool. Considering the cost to generate population-scale sequence data and the biased detection of variants using chip data, developing an effective genotyping strategy using low-coverage whole-genome sequencing (LCS) data is necessary to conduct genetic analyses.ResultsDifferent genotype imputation strategies (BaseVar + STITCH, Bcftools + Beagle4, and GATK + Beagle5), sequencing coverages (0.1X, 0.5X, 1.0X, 1.5X, and 2.0X), and sample sizes (100, 200, 300, 400, 500, and 600) were compared. Our results showed that using BaseVar + STITCH at a sequencing depth of 1.0X with a sample size larger than 300 resulted in the highest genotyping accuracy, with a genotype concordance higher than 98.8% and genotype accuracy higher than 0.97. We performed multivariate genome-wide association studies (GWAS), followed by conditional GWAS and estimation of the confidence intervals of quantitative trait loci (QTL) to investigate the genetic architecture of wool traits. Six QTL were detected, which explained 0.4 to 7.5% of the phenotypic variation. Gene-level mapping identified the fibroblast growth factor 10 (FGF10) gene as associated with fiber growth and diameter, which agrees with previous results from functional data analyses on the FGF gene family in other species, and is relevant for wool rabbit breeding.ConclusionsWe suggest that LCS followed by imputation can be a cost-effective alternative to array and high-depth sequencing for assessing common variants. GWAS combined with LCS can identify new QTL and candidate genes that are associated with quantitative traits. This study provides a cost-effective and powerful method for investigating the genetic architecture of complex traits, which will be useful for genomic breeding applications.

Rapid and Reproducible Karyotyping with Nanopore Sequencing in AML Patients

Introduction: We evaluated low coverage whole genome sequencing (WGS) of acute myeloid leukemia (AML) patients using long read Oxford Nanopore Technology (ONTseq) for karyotyping and compared the results and the frequency of patients with myelodysplasia-related cytogenetic abnormalities according to previous and current AML classifications with conventional cytogenetics (CG). Methods: Diagnostic samples from peripheral blood or bone marrow of 100 AML patients were sequenced in 3 medical centers on a GridION sequencer using SQK-LSK109 kits according to the manufacturer's instructions, and analyzed by CG. Ten samples were sequenced independently in two different laboratories. Fifty samples were sequenced prospectively from newly diagnosed de novo AML patients. Sequencing reads were analyzed by an in-house bioinformatics pipeline based on publicly available tools such as nglmr aligner, samtools, igv_tools, R package ACE, the CyDAS webserver, and custom scripts for transforming ACE copy number models to karyotype formulas. CNVs from ONTseq data were mapped to 290 chromosome bands to allow comparison with CG. We compared the following cytogenetic categories: Complex karyotype (cKT) ≥ 3 chromosomal abnormalities; monosomal karyotype (mKT) as defined by Breems et al.; karyotype with myelodysplasia-related cytogenetic abnormalities that defines MRC-AML according WHO 2016 classification (MRC-KT), and ICC 2022 classification (MRCA-KT). Results: ONTseq of 100 AML patients with a 4.4-fold genome coverage (range: 2.1-6.2) allowed us to resolve CNVs at a high resolution of ≥0.1 mega-basepairs (Mbp). Analysis of the ten duplicate samples established a high reproducibility of ONTseq. 110 CNVs were detected by both laboratories at identical chromosome positions and similar length (R2=0.999). Only 8 CNVs (7%) differed considerably in length between the replicates (median length difference 2 Mbp, range 0.4-16 Mbp). Of 100 patients with a median age of 65 years (range 18-97), ≥1 chromosome band was gained or lost in 69 patients according to ONTseq analysis, while CNVs or balanced translocations were found by cytogenetics in 72 patients. Five patients had a normal karyotype by ONTseq, but not by CG, while two patients had a normal karyotype by CG but not by ONTseq. Two of these 7 discrepant patients had translocations as their sole cytogenetic abnormality [t(8;21) and t(9;11)], which cannot be detected by low coverage genome ONTseq. Patients with chromosomal abnormalities had a median of 29 (range 1-140) and 30 (range 3-136) chromosomal bands with CNVs according to ONTseq and CG, respectively. Considering all patients, gains and losses at the level of chromosomal bands correlated very well (gains: R2=0.922; losses: R2=0.923) (Figure 1). All patients were then classified according the definitions for cKT, mKT, MRC-KT, and MRCA-KT (Table 1). Overall, patients were similarly classified by ONTseq and CG results (P<0.001). MRC and MRCA-KTs were consistently identified by ONTseq and CG in 57 patients. In 2 and 2 patients, MRC and MRCA-KTs, respectively, were identified by CG only. In 1 and 1 patient, MRC and MRCA-KT, respectively, was identified by ONTseq only. The largest discrepancies were found for mKTs due to the higher resolution of ONTseq, which more often identified subchromosomal instead of whole chromosome CNVs compared to CG, which were not considered for mKT. Fifty of the 100 patients were sequenced prospectively at time of diagnosis. The median age of these patients was 70 years (range 61-97) and all patients had de novo AML. At least one CNV was detected by ONTseq in 24 (48%) and by CG in 26 (52%) patients. cKT, mKT, MRC-KT, and MRCA-KT were detected by ONTseq in 12, 7, 17, and 17 patients and by CG in 14, 11, 17, and 17 patients, respectively. The median time for library preparation for prospectively analyzed patients was 3:45 hours, for ONTseq 24 hours and for bioinformatic analysis 6:30 hours. In this setting, clinical reports can be delivered on the fourth day of sample receipt. Conclusion: Low coverage WGS with ONT provides a highly reproducible method for rapid karyotyping with high resolution. The identification of cKT and myelodysplasia-related cytogenetic abnormalities correlated very well with conventional cytogenetics, even though balanced translocations cannot be detected by our ONTseq protocol. Low coverage WGS is a promising approach for diagnostic karyotyping in AML patients. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

EPCO-47. ITCC-P4: GENOMIC PROFILING AND ANALYSES OF PEDIATRIC PATIENT TUMOR AND PATIENT-DERIVED XENOGRAFT (PDX) MODELS FOR HIGH THROUGHPUT IN VIVO TESTING

Abstract Advancements in state-of-the-art molecular profiling techniques has resulted in better understanding of pediatric cancers and their drivers. Conversely, it also became apparent that pediatric cancers are much more heterogeneous than previously thought. Many new types and subtypes of pediatric cancers have been identified with distinct molecular and clinical characteristics. However, for most newly recognized entities there is no specific treatment available yet. The ITCC-P4 consortium is a collaboration between many academic centers across Europe and several pharmaceutical companies involved in preclinical testing, with the overall aim to establish a sustainable platform of &amp;gt;400 molecularly well-characterized PDX models of high-risk pediatric cancers and to use them for in vivo testing of novel mechanism-of-action based treatments. Currently, 340 models are fully established, including 87 brain and 253 non-brain tumor models, together representing different tumor types both from primary (113) and relapsed (92)/metastatic disease (42). 252 of these models have been fully molecularly characterized, representing 18 pediatric cancer entities and 43 different subtypes. Using low coverage whole-genome and whole exome sequencing, somatic mutation calling, DNA copy number, transcriptome analysis and methylation profiling we have observed that the molecular profile of most PDX models closely mimics their original tumors. Clonal evolution of somatic variants was only observed in some PDX-tumor pairs or so between disease states. Somatic copy number variant analysis highlights specific alterations for instance MYB, MYC, MYCN, NTRK3, PTEN loss differently distributed between PDX-patient tumor pairs in high-grade gliomas. Overall, our results show that we have established &amp;gt;250 PDX models of solid pediatric cancers, that well represents the disease spectrum and that is currently being used for in vivo testing of standard of care drugs and targeted small molecules. Treatment responses will be directly linked to molecular data to identify potential biomarkers for prioritization or deprioritization of individual, patient-specific specific drugs.

Identification of RP1 as the genetic cause of retinitis pigmentosa in a multi-generational pedigree using Extremely Low-Coverage Whole Genome Sequencing (XLC-WGS)

Motivation.Next-generation sequencing (NGS) technologies are decisive for discovering disease-causing variants, although their cost limits their utility in a clinical setting. A cost-mitigating alternative is an extremely low coverage whole-genome sequencing (XLC-WGS). We investigated its use to identify causal variants within a multi-generational pedigree of individuals with retinitis pigmentosa (RP). Causing progressive vision loss, RP is a group of genetically heterogeneous eye disorders with approximately 60 known causal genes. ResultsWe performed XLC-WGS in seventeen members of this pedigree, including three individuals with a confirmed diagnosis of RP. Sequencing data were processed using Illumina's DRAGEN pipeline and filtered using Illumina's genotype quality score metric (GQX). The resulting variants were analyzed using Expert Variant Interpreter (eVai) from enGenome as a prioritization tool. A nonsense known mutation (c.1625C > G; p.Ser542*) in exon 4 of the RP1 gene emerged as the most likely causal variant. We identified two homozygous carriers of this variant among the three sequenced RP cases and three heterozygous individuals with sufficient coverage of the RP1 locus. Our data show the utility of combining pedigree information with XLC-WGS as a cost-effective approach to identify disease-causing variants.

Washout DNA copy number analysis by low-coverage whole genome sequencing for assessment of thyroid FNAs.

BackgroundPapillary thyroid microcarcinoma (PTMC) is defined as a papillary carcinoma measuring ≤ 10 mm. The current management of PTMC has become more conservative; however, there are high-risk tumor features that can be revealed only postoperatively. For thyroid cancer, BRAF mutations and somatic copy number variation (CNV) are the most common genetic events. Molecular testing may contribute to clinical decision-making by molecular risk stratification, for example predicting lymph node (LN) metastasis. Here, we build a risk stratification model based on molecular profiling of thyroid fine needle aspiration (FNA) washout DNA (wDNA) for the differential diagnosis of thyroid nodules.MethodsFifty-eight patients were recruited, FNA wDNA samples were analyzed using CNV profiling through low-coverage whole genome sequencing (LC-WGS) and BRAF mutation was analyzed using quantitative PCR. FNA pathology was reported as a Bethesda System for Reporting Thyroid Cytopathology (BSRTC) score. Ultrasound examination produced a Thyroid Imaging Reporting and Data System (TIRADS) score.ResultsIn total, 37 (63.8%) patients with a TIRADS score of 4A, 13 (22.4%) patients with a TIRADS score of 4B, and 8 (13.8%) patients with a TIRADS score of 4C were recruited after ultrasound examination. All patients underwent FNA with wDNA profiling. CNVs were identified in 17 (29.3%) patients. CNVs were frequent in patients with a BSRTC score of V or VI, including eight (47.1%) patients with a score of VI and five (29.4%) with a score of V, but not in patients with a score of III, II, or I (0%). BRAF mutation was not significantly correlated with BSRTC score. LN metastasis was found more frequently in CNV-positive (CNV+) than in CNV-negative (CNV–) patients (85.7% vs. 34.6%, odds ratio = 11.33, p = 0.002). In total, three molecular subtypes of thyroid nodules were identified in this study: 1) CNV+, 2) CNV– and BRAF positive (BRAF+), and 3) CNV– and BRAF negative (BRAF–). For the CNV+ subtype, 10 (83.3%) lesions with LN metastasis were found, including four (100%) small lesions (i.e. ≤ 5 mm). For the CNV– and BRAF+ nodules, LN metastases were detected in only seven (60.0%) larger tumors (i.e. > 5 mm). For CNV– and BRAF– tumors, LN metastasis was also frequently found in larger tumors only.ConclusionsIt is feasible to identify high-risk LN metastasis thyroid cancer from FNA washout samples preoperatively using wDNA CNV profiling using LC-WGS.

Genetic Mapping of Multiple Traits Identifies Novel Genes for Adiposity, Lipids, and Insulin Secretory Capacity in Outbred Rats.

Despite the successes of human genome-wide association studies, the causal genes underlying most metabolic traits remain unclear. We used outbred heterogeneous stock (HS) rats, coupled with expression data and mediation analysis, to identify quantitative trait loci (QTLs) and candidate gene mediators for adiposity, glucose tolerance, serum lipids, and other metabolic traits. Physiological traits were measured in 1,519 male HS rats, with liver and adipose transcriptomes measured in >410 rats. Genotypes were imputed from low-coverage whole-genome sequencing. Linear mixed models were used to detect physiological and expression QTLs (pQTLs and eQTLs, respectively), using both single nucleotide polymorphism (SNP)- and haplotype-based models for pQTL mapping. Genes with cis-eQTLs that overlapped pQTLs were assessed as causal candidates through mediation analysis. We identified 14 SNP-based pQTLs and 19 haplotype-based pQTLs, of which 10 were in common. Using mediation, we identified the following genes as candidate mediators of pQTLs: Grk5 for fat pad weight and serum triglyceride pQTLs on Chr1, Krtcap3 for fat pad weight and serum triglyceride pQTLs on Chr6, Ilrun for a fat pad weight pQTL on Chr20, and Rfx6 for a whole pancreatic insulin content pQTL on Chr20. Furthermore, we verified Grk5 and Ktrcap3 using gene knockdown/out models, thereby shedding light on novel regulators of obesity.

Detection and monitoring of HBV-related hepatocellular carcinoma from plasma cfDNA fragmentation profiles

Most hepatocellular carcinomas (HCCs) are associated with hepatitis B virus infection (HBV) in China. Early detection of HCC can significantly improve prognosis but is not yet fully clinically feasible. This study aims to develop methods for detecting HCC and studying the carcinogenesis of HBV using plasma cell-free DNA (cfDNA) whole-genome sequencing (WGS) data. Low coverage WGS was performed for 452 participants, including healthy individuals, hepatitis B patients, cirrhosis patients, and HCC patients. Then the sequencing data were processed using various machine learning models based on cfDNA fragmentation profiles for cancer detection. Our best model achieved a sensitivity of 87.10% and a specificity of 88.37%, and it showed an increased sensitivity with higher BCLC stages of HCC. Overall, this study proves the potential of a non-invasive assay based on cfDNA fragmentation profiles for the detection and prognosis of HCC and provides preliminary data on the carcinogenic mechanism of HBV.

Haff disease associated with consumption of buffalofish (Ictiobus spp.) in the United States, 2010–2020, with confirmation of the causative species

Background In the United States, buffalofish (Ictiobus spp.) are sporadically associated with sudden onset muscle pain and weakness due to rhabdomyolysis within 24 h of fish consumption (Haff disease). Previous genetic analyses of case-associated samples were unable to distinguish the three species of buffalofish that occur in the US, Ictiobus cyprinellus (bigmouth buffalo), Ictiobus bubalus (smallmouth buffalo), and Ictiobus niger (black buffalo). Methods Ten events were investigated between 2010 and 2020 and demographic and clinical information was collected for 24 individuals. Meal remnants were collected from 5 of 10 events with additional associated samples (n = 24) collected from another five of 10 events. Low-coverage whole-genome sequencing (genome skimming) was used to identify meal remnants. Results Patients (26–75 years of age) ranged from 1–4 per event, with 90% involving ≥2 individuals. Reported symptoms included muscle tenderness and weakness, nausea/vomiting, and brown/tea-colored urine. Median incubation period was 8 h. Ninety-six percent of cases were hospitalized with a median duration of four days. The most commonly reported laboratory finding was elevated creatine phosphokinase and liver transaminases. Treatment was supportive including intravenous fluids to prevent renal failure. Events occurred in California (1), Illinois (2), Louisiana (1), New York (1), Mississippi (1), Missouri (2), New Jersey (1), and Texas (1) with location of harvest, when known, being Illinois, Louisiana, Mississippi, Missouri, Texas, and Wisconsin. Meal remnants were identified as I. bubalus (n = 4) and I. niger (n = 1). Associated samples were identified as I. bubalus (n = 16), I. cyprinellus (n = 5), and I. niger (n = 3). Discussion Time course, presentation of illness, and clinical findings were all consistent with previous domestic cases of buffalofish-associated Haff disease. In contrast to previous reports that I. cyprinellus is the causative species in US cases, data indicate that all three buffalofish species are harvested but I. bubalus is most often associated with illness.

Performance of whole-genome promoter nucleosome profiling of maternal plasma cell-free DNA for prenatal noninvasive prediction of fetal macrosomia: a retrospective nested case-control study in mainland China

BackgroundFetal macrosomia is common occurrence in pregnancy, which is associated with several adverse prognosis both of maternal and neonatal. While, the accuracy of prediction of fetal macrosomia is poor. The aim of this study was to develop a reliable noninvasive prediction classifier of fetal macrosomia.MethodsA total of 3600 samples of routine noninvasive prenatal testing (NIPT) data at 12+ 0–27+ 6 weeks of gestation, which were subjected to low-coverage whole-genome sequencing of maternal plasma cell-free DNA (cfDNA), were collected from three independent hospitals. We identified set of genes with significant differential coverages by comparing the promoter profiling between macrosomia cases and controls. We selected genes to develop classifier for noninvasive predicting, by using support vector machine (SVM) and logistic regression models, respectively. The performance of each classifier was evaluated by area under the curve (AUC) analysis.ResultsAccording to the available follow-up results, 162 fetal macrosomia pregnancies and 648 matched controls were included. A total of 1086 genes with significantly differential promoter profiling were found between pregnancies with macrosomia and controls (p < 0.05). With the AUC as a reference，the classifier based on SVM (CMA-A2) had the best performance, with an AUC of 0.8256 (95% CI: 0.7927–0.8586).ConclusionsOur study provides that assessing the risk of fetal macrosomia by whole-genome promoter nucleosome profiling of maternal plasma cfDNA based on low-coverage next-generation sequencing is feasible.

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

SummaryThe 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.

Molecular heterogeneity and commonalities in pancreatic cancer precursors with gastric and intestinal phenotype

ObjectiveDue to the limited number of modifiable risk factors, secondary prevention strategies based on early diagnosis represent the preferred route to improve the prognosis of pancreatic ductal adenocarcinoma (PDAC). Here,...

Whole-genome/exome analysis of circulating tumor DNA and comparison to tumor genomics from patients with heavily pre-treated ovarian cancer: subset analysis of the PERMED-01 trial

IntroductionThe poor prognosis of ovarian carcinoma (OvC) is due to the advanced stage at diagnosis, a high risk of relapse after first-line therapies, and the lack of efficient treatments in the recurrence setting. Circulating tumor DNA (ctDNA) analysis is a promising tool to assess treatment-resistant OvC and may avoid iterative tissue biopsies. We aimed to evaluate the genomic profile of recurrent heavily pre-treated OvC.MethodsWe performed tumor panel-based sequencing as well as low-coverage whole-genome sequencing (LC-WGS) of tumor and plasma collected in patients with ovarian cancer included in the PERMED-01 trial. Whole-exome sequencing (WES) data of plasma samples were also analyzed and compared to mutation and copy number alteration (CNA) tumor profiles. The prognostic value [progression-free survival (PFS)] of these alterations was assessed in an exploratory analysis.ResultsTumor and plasma genomic analyses were done for 24 patients with heavily pretreated OvC [67% high-grade serous carcinoma (HGSC)]. Tumor mutation burden was low (median 2.04 mutations/Mb) and the most frequent mutated gene was TP53 (94% of HGSC). Tumor CNAs were frequent with a median of 50% of genome altered fraction. Plasma LC-WGS and WES detected ctDNA in 21/24 cases (88%) with a median tumor fraction of 12.7%. We observed a low correlation between plasma and tumor CNA profiles. However, this correlation was significant in cases with the highest circulating tumor fraction. Plasma genome altered fraction and plasma mutation burden (p = 0.011 and p = 0.041, respectively, log-rank tests) were associated with PFS.ConclusionsCombination of LC-WGS and WES can detect ctDNA in most pre-treated OvCs. Some ctDNA characteristics, such as genome altered fraction and plasma mutation burden, showed prognostic value. ctDNA assessment with LC-WGS may be a promising and non-expansive tool to evaluate disease evolution in this disease with high genomic instability.Clinical Trial Registration https://clinicaltrials.gov/ct2/show/NCT02342158, identifier NCT02342158.

Highly precise breakpoint detection of chromosome balanced translocation in chronic myelogenous leukaemia: Case series.

Chronic myelogenous leukaemia (CML) has a special phenomenon of chromosome translocation, which is called Philadelphia chromosome translocation. However, the detailed connection of this structure is troublesome and expensive to be identified. Low‐coverage whole genome sequencing (LCWGS) could not only detect the previously unknown chromosomal translocation, but also provide the breakpoint candidate small region (with an accuracy of ±200 bases). Importantly, the sequencing cost of LCWGS is about US$300. Then, with the Sanger DNA sequencing, the precise breakpoint can be determined at a single base level. In our project, with LCWGS, BCR and ABL1 are successfully identified to be disrupted in three CML patients (at chr22:23,632,356 and chr9:133,590,450; chr22:23,633,748 and chr9:133,635,781; chr22: 23,631,831 and chr9:133,598,513, respectively). Due to the reconnection after chromosome breakage, classical fusion gene (BCR::ABL1) was found in bone marrow and peripheral blood. The precise breakpoints were helpful to investigate the pathogenic mechanism of CML and could better guide the classification of CML subtypes. This LCWGS method is universal and can be used to detect all diseases related to chromosome variation, such as solid tumours, liquid tumours and birth defects.

CMDB: the comprehensive population genome variation database of China.

A high-quality genome variation database derived from a large-scale population is one of the most important infrastructures for genomics, clinical and translational medicine research. Here, we developed the Chinese Millionome Database (CMDB), a database that contains 9.04 million single nucleotide variants (SNV) with allele frequency information derived from low-coverage (0.06×-0.1×) whole-genome sequencing (WGS) data of 141 431 unrelated healthy Chinese individuals. These individuals were recruited from 31 out of the 34 administrative divisions in China, covering Han and 36 other ethnic minorities. CMDB, housing the WGS data of a multi-ethnic Chinese population featuring wide geographical distribution, has become the most representative and comprehensive Chinese population genome database to date. Researchers can quickly search for variant, gene or genomic regions to obtain the variant information, including mutation basic information, allele frequency, genic annotation and overview of frequencies in global populations. Furthermore, the CMDB also provides information on the association of the variants with a range of phenotypes, including height, BMI, maternal age and twin pregnancy. Based on these data, researchers can conduct meta-analysis of related phenotypes. CMDB is freely available at https://db.cngb.org/cmdb/.