Local exome sequences facilitate imputation of less common variants and increase power of genome wide association studies

The analysis of less common variants in genome-wide association studies promises to elucidate complex trait genetics but is hampered by low power to reliably detect association. We show that addition of population-specific exome sequence data to global reference data allows more accurate imputation, particularly of less common SNPs (minor allele frequency 1–10%) in two very different European populations. The imputation improvement corresponds to an increase in effective sample size of 28–38%, for SNPs with a minor allele frequency in the range 1–3%

A somatic-mutational process recurrently duplicates germline susceptibility loci and tissue-specific super-enhancers in breast cancers

Somatic rearrangements contribute to the mutagenized landscape of cancer genomes. Here, we systematically interrogated rearrangements in 560 breast cancers by using a piecewise constant fitting approach. We identified 33 hotspots of large (>100 kb) tandem duplications, a mutational signature associated with homologous-recombination-repair deficiency. Notably, these tandem-duplication hotspots were enriched in breast cancer germline susceptibility loci (odds ratio (OR) = 4.28) and breast-specific 'super-enhancer' regulatory elements (OR = 3.54). These hotspots may b

Whole exome sequencing in an isolated population from the Dalmatian island of Vis

We have whole-exome sequenced 176 individuals from the isolated population of the island of Vis in Croatia in order to describe exonic variation architecture. We found 290 577 single nucleotide variants (SNVs), 65% of which are singletons, low frequency or rare variants. A total of 25 430 (9%) SNVs are novel, previously not catalogued in NHLBI GO Exome Sequencing Project, UK10K-Generation Scotland, 1000Genomes Project, ExAC or NCBI Reference Assembly dbSNP. The majority of these variants (76%) are singletons. Comparable to data obtained from UK10K-Generation Scotland that were sequenced and analysed using the same protocols, we detected an enrichment of potentially damaging variants (non-synonymous and loss-of-function) in the low frequency and common variant categories. On average 115 (range 93–140) genotypes with loss-of-function variants, 23 (15–34) of which were homozygous, were identified per person. The landscape of loss-of-function variants across an exome revealed that variants mainly accumulated in genes on the xenobiotic-related pathways, of which majority coded for enzymes. The frequency of loss-of-function variants was additionally increased in Vis runs of homozygosity regions where variants mainly affected signalling pathways. This work confirms the isolate status of Vis population by means of whole-exome sequence and reveals the pattern of loss-of-function mutations, which resembles the trails of adaptive evolution that were found in other species. By cataloguing the exomic variants and describing the allelic structure of the Vis population, this study will serve as a valuable resource for future genetic studies of human diseases, population genetics and evolution in this population

Sex differences in oncogenic mutational processes.

Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here we report a pan-cancer analysis of sex differences in whole genomes of 1983 tumours of 28 subtypes as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in molecular cancer research

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Abstract 2801: Detection of biallelic loss of DNA repair genes in formalin-fixed, paraffin embedded (FFPE) tumor samples using a novel tumor-only sequencing panel with error correction

Abstract Background: Loss-of-function (LOF) mutations in DNA damage response (DDR) tumor suppressor genes are compensated for by functional redundancies, exposing synthetic lethal (SL) interactions and opportunities for targeted therapy. Patient selection for SL-based therapy may be improved by assessment of gene-specific loss of heterozygosity (LOH) and biallelic LOF, neither of which is routinely reported by existing targeted sequencing panels. The Synthetic Lethal Interactions for Precision Diagnostics (SNiPDx) targeted sequencing panel features a novel bioinformatic analysis pipeline that enables accurate genome-wide determination of allele-specific copy number, estimation of tumor ploidy and purity, and detection of single nucleotide and indel variants in target genes focused on DDR pathways, all from tumor-only samples. Here we describe the development and accuracy of SNiPDx for detection of LOH and bi-allelic LOF genetic alterations in FFPE samples. Methods: Genomic DNA (&amp;gt;50 ng) was extracted from FFPE samples of multiple solid tumor types (n = 43). Next-generation sequencing was performed on anchored multiplex PCR libraries, constructed using probes that incorporate unique molecular identifiers and span 26 genes and 5,000 genome-wide common germline single-nucleotide polymorphisms (SNPs). Unmatched non-tumor samples (n = 24) were used to generate a reference baseline dataset. The FACETS algorithm, optimized to account for differential DNA fragmentation across samples, was used to assess copy number imbalance in heterozygous SNPs and to quantify tumor purity. Allele fractions at each heterozygous SNP were used to estimate allelic imbalances across chromosomal regions. A reference dataset was derived from matched FFPE tumor samples by whole genome sequencing (WGS) and analysis of sequence data using 3 complementary algorithms. Allele-specific copy number analysis and tumor purity estimation from SNiPDx and WGS data were compared. Results: Copy number was evaluable in 605 genes from 24 matched tumor samples that passed quality control filters. Median sequencing depth across samples by SNiPDx and WGS were 1346x and 18.6x, respectively. LOH detection by SNiPDx was reproducible (100%) across 170 genes from 7 samples sequenced and analyzed in duplicate. A strong correlation was observed between sample purity estimates by WGS and SNiPDx (Pearson’s r = 0.81, p &amp;lt; 0.001). Compared with WGS-derived calls, the sensitivity and specificity of LOH detection by SNiPDx were 95% and 90%, respectively, rising to 97% and 91% in regions with LOH agreement by all 3 WGS algorithms, and to 99% and 97% in diploid regions with no subclonal alterations. b The SNiPDx panel is a novel clinical test for biallelic loss in FFPE tumor-only samples with high accuracy as validated through concordance with a WGS-derived dataset. Citation Format: Dominik Glodzik, Pier Selencia, Ryan Rogge, Ian M. Silverman, Michael Zinda, Maria Koehler, Robert D. Daber, Verity Johnson, Jorge S. Reis-Filho, Victoria Rimkunas. Detection of biallelic loss of DNA repair genes in formalin-fixed, paraffin embedded (FFPE) tumor samples using a novel tumor-only sequencing panel with error correction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2801.</jats:p

Revealing the impact of recurrent and rare structural variants in multiple myeloma

SummaryThe landscape of structural variants (SVs) in multiple myeloma remains poorly understood. Here, we performed comprehensive classification and analysis of SVs in multiple myeloma, interrogating a large cohort of 762 patients with whole genome and RNA sequencing. We identified 100 SV hotspots involving 31 new candidate driver genes, including drug targets BCMA (TNFRSF17) and SLAMF7. Complex SVs, including chromothripsis and templated insertions, were present in 61 % of patients and frequently resulted in the simultaneous acquisition of multiple drivers. After accounting for all recurrent events, 63 % of SVs remained unexplained. Intriguingly, these rare SVs were associated with up to 7-fold enrichment for outlier gene expression, indicating that many rare driver SVs remain unrecognized and are likely important in the biology of individual tumors.</jats:p

Mean accuracy of imputation (r² of allelic dosage across all samples for a SNP) averaged across SNPs split by Minor Allele Frequency (MAF).

<p>MAF bins increase by factors of √10, to create four exponentially increasing bins.</p><p>N SNPs: number of SNPs in MAF bin.</p><p>1kG: 1000 Genomes used as reference panel.</p><p>1kG+LRP: 1000 Genomes plus local reference panel.</p><p>Increase r²: Average across all SNPs in MAF bin increase in r².</p><p>Std dev: The standard deviation (across SNPs) of the increase in r² at each SNP.</p><p>Inc. Sample: Increase in effective sample size for GWAS.</p><p>The standard errors of mean increases are less than 0.003. All improvements in r² are significantly different from zero and significantly different between MAF bands (P<0.001, two-sided t tests).</p

Preparation of array data and local reference panel for imputation.

<p>The genotype data were quality controlled and phased. These data were then used in further downstream analysis.</p

Plot of mean improvement in imputation accuracy (r²) for SNPs with minor allele frequency (MAF) in the range 1–10% in our exome sequence data.

<p>Plot of mean improvement in imputation accuracy (r²) for SNPs with minor allele frequency (MAF) in the range 1–10% in our exome sequence data.</p