Search bioRxiv⌕ Search

bioRxiv · 10.1101/322180

Custom hereditary breast cancer gene panel selectively amplifies target genes for reliable variant calling

Abstract

Background: Target enrichment coupled with next generation sequencing provide high-throughput approaches for screening several genes of interest. These approaches facilitate screening a panel of genes for mutations associated with inherited breast cancer for research, diagnostic, and genetic counseling applications.\n\nObjective: To evaluate the performance of our custom 13 gene breast cancer panel, based on singleplex PCR, developed by WaferGen BioSystems. The panel was evaluated using patient-derived DNA samples, in terms of target enrichment efficiency, off-target enrichment, uniformity of target capture, effect of GC content of target regions on coverage depth, and concordance with validated variant calls.\n\nResults: At least 90% of target sequence for each gene was captured at 30x or greater. We evaluated uniformity of target capture across samples by calculating the percentage of samples with at least 90% of total target captured at 100x or greater and found 92% (33/36 samples) uniformity for our panel. Off-target enrichment ranges between 7.2% and 22.3%. We found perfect concordance between our custom panel and the Qiagen human breast cancer panel for functionally annotated variant calls in high read depth shared target regions. Altogether, there was agreement between the panels for 779 variants at 41 loci. We also confirmed 10 pathogenic mutations, initially discovered by Sanger sequencing, in the appropriate samples following target enrichment using our custom WaferGen panel.\n\nConclusion: Our custom hereditary breast cancer panel is sensitive to the desired target genes and facilitates deep sequencing for reliable variant calling.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Amuzu, S., Revil, T., Foulkes, W. D., Ragoussis, J.. 2018-05-15. Custom hereditary breast cancer gene panel selectively amplifies target genes for reliable variant calling. https://doi.org/10.1101/322180

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Cellular context shapes the transcriptomic impact of somatic variants

Somatic SNVs are commonly prioritized by recurrence or sequence annotation, leaving the molecular consequences of private variants unresolved. We developed PrismSNV and Ligo-Seq, a single-cell full-length transcriptome sequencing platform, to quantify transcriptomic displacement associated with within-cell SNV-state toggling. Across 26 samples from three cancer types, more than 60% of candidate SNVs showed significant cell-type-specific displacement with over 30% variation in magnitude. Sequence-based deleteriousness was poorly aligned with cellular displacement, and fewer than 3% of top-displacement SNVs across six tumor types were catalogued in ClinVar. Displacement was strongest in transcriptionally plastic, less differentiated states. Analysis of 655,349 CD4+ T-cell transcriptomes further showed that SNV-gene associations changed in magnitude and sometimes direction during activation. Lack of recurrence or clinical annotation does not imply transcriptomic inertness; variant impact is shaped by cellular context rather than sequence alone, establishing the variant-cell pair as a critical unit for interpreting somatic variation.

genetics↗

Genetic and Epigenetic Landscape of Self-Identified Hispanics/Latino in All Of Us

Human genetic studies still underrepresent many populations, limiting discovery and interpretation of medically relevant variation. Extensive admixture in self-identified Hispanic/Latino populations increases the need for representative reference data and ancestry-aware interpretation of medically relevant variation. We used long-read sequencing to analyze genetic variation, ancestry, and DNA methylation in 1,490 self-identified Hispanic/Latino participants from the All of Us Research Program. We identified 10 million novel variants and more than 100 candidate phenotype associations, with fine-mapping prioritizing structural variants over nearby single-nucleotide variants in more than half. We linked 3,329 tandem repeats to DNA methylation, resolved widespread allele-specific methylation, and identified ancestry-linked regulation across paralogous genes. These results reveal previously inaccessible genetic and regulatory variation and show the value of long-read sequencing for diverse populations.

genetics↗

The genetic architecture of human DNA methylome variation

Population DNA methylome analyses remain largely confined to reference-defined genomic space despite extensive human genetic diversity. A central challenge is therefore to define comparable methylation phenotypes across diverse genomes and map the genetic architecture of methylome variation. Here, using 460 haplotypes from the Human Pangenome Reference Consortium Release 2, we develop two complementary pangenome-based frameworks: one compares methylation within homologous regions while retaining haplotype-specific structural configuration and CpG content; the other represents CpG island (CGI) variation at homologous loci as sequence-resolved alleles. We identified 47,039 CGI alleles, including 8,040 absent from CHM13; 62% were attributable to structural variants and 31% to small variants. Through local genealogies, we reconstructed CGI gain, loss and turnover, revealing how present-day methylation differences align with CGI evolutionary trajectories. We found that genome-wide, methylation variability was buffered against genetic diversity across more than 90% of the genome, with exceptions at extreme nucleotide diversity and locally around structural variants. Our methylation framework enabled population-scale allele-aware methylation quantitative trait locus (mQTL) mapping into non-reference and structurally complex sequence, where mQTL-associated intervals were enriched. mQTLs linked methylation to transcriptomic phenotypes, population differentiation and complex traits, supporting 8% of population differentially methylated regions and colocalizing with more than half of genome-wide association study loci. Together, our study redefines human methylome analysis and our findings reveal a genome-wide architecture of genetic-epigenetic coupling, reshaping how methylation variation is interpreted across human development and disease.

genetics↗