Efficient and flexible Integration of variant characteristics in rare variant association studies using integrated nested Laplace approximation.

Rare variants are thought to play an important role in the etiology of complex diseases and may explain a significant fraction of the missing heritability in genetic disease studies. Next-generation sequencing facilitates the association of rare variants in coding or regulatory regions with complex diseases in large cohorts at genome-wide scale. However, rare variant association studies (RVAS) still lack power when cohorts are small to medium-sized and if genetic variation explains a small fraction of phenotypic variance. Here we present a novel Bayesian rare variant Association Test using Integrated Nested Laplace Approximation (BATI). Unlike existing RVAS tests, BATI allows integration of individual or variant-specific features as covariates, while efficiently performing inference based on full model estimation. We demonstrate that BATI outperforms established RVAS methods on realistic, semi-synthetic whole-exome sequencing cohorts, especially when using meaningful biological context, such as functional annotation. We show that BATI achieves power above 70% in scenarios in which competing tests fail to identify risk genes, e.g. when risk variants in sum explain less than 0.5% of phenotypic variance. We have integrated BATI, together with five existing RVAS tests in the 'Rare Variant Genome Wide Association Study' (rvGWAS) framework for data analyzed by whole-exome or whole genome sequencing. rvGWAS supports rare variant association for genes or any other biological unit such as promoters, while allowing the analysis of essential functionalities like quality control or filtering. Applying rvGWAS to a Chronic Lymphocytic Leukemia study we identified eight candidate predisposition genes, including EHMT2 and COPS7A.

Allele balance bias identifies systematic genotyping errors and false disease associations.

In recent years, next-generation sequencing (NGS) has become a cornerstone of clinical genetics and diagnostics. Many clinical applications require high precision, especially if rare events such as somatic mutations in cancer or genetic variants causing rare diseases need to be identified. Although random sequencing errors can be modeled statistically and deep sequencing minimizes their impact, systematic errors remain a problem even at high depth of coverage. Understanding their source is crucial to increase precision of clinical NGS applications. In this work, we studied the relation between recurrent biases in allele balance (AB), systematic errors, and false positive variant calls across a large cohort of human samples analyzed by whole exome sequencing (WES). We have modeled the AB distribution for biallelic genotypes in 987 WES samples in order to identify positions recurrently deviating significantly from the expectation, a phenomenon we termed allele balance bias (ABB). Furthermore, we have developed a genotype callability score based on ABB for all positions of the human exome, which detects false positive variant calls that passed state-of-the-art filters. Finally, we demonstrate the use of ABB for detection of false associations proposed by rare variant association studies. Availability: https://github.com/Francesc-Muyas/ABB.

Investigating the effects of additional truncating variants in DNA-repair genes on breast cancer risk in BRCA1-positive women.

BACKGROUND: Inherited pathogenic variants in BRCA1 and BRCA2 are the most common causes of hereditary breast and ovarian cancer (HBOC). The risk of developing breast cancer by age 80 in women carrying a BRCA1 pathogenic variant is 72%. The lifetime risk varies between families and even within affected individuals of the same family. The cause of this variability is largely unknown, but it is hypothesized that additional genetic factors contribute to differences in age at onset (AAO). Here we investigated whether truncating and rare missense variants in genes of different DNA-repair pathways contribute to this phenomenon. METHODS: We used extreme phenotype sampling to recruit 133 BRCA1-positive patients with either early breast cancer onset, below 35 (early AAO cohort) or cancer-free by age 60 (controls). Next Generation Sequencing (NGS) was used to screen for variants in 311 genes involved in different DNA-repair pathways. RESULTS: Patients with an early AAO (73 women) had developed breast cancer at a median age of 27 years (interquartile range (IQR); 25.00-27.00 years). A total of 3703 variants were detected in all patients and 43 of those (1.2%) were truncating variants. The truncating variants were found in 26 women of the early AAO group (35.6%; 95%-CI 24.7 - 47.7%) compared to 16 women of controls (26.7%; 95%-CI 16.1 to 39.7%). When adjusted for environmental factors and family history, the odds ratio indicated an increased breast cancer risk for those carrying an additional truncating DNA-repair variant to BRCA1 mutation (OR: 3.1; 95%-CI 0.92 to 11.5; p-value = 0.07), although it did not reach the conventionally acceptable significance level of 0.05. CONCLUSIONS: To our knowledge this is the first time that the combined effect of truncating variants in DNA-repair genes on AAO in patients with hereditary breast cancer is investigated. Our results indicate that co-occurring truncating variants might be associated with an earlier onset of breast cancer in BRCA1-positive patients. Larger cohorts are needed to confirm these results.

Analysis of a long-term outbreak of XDR Pseudomonas aeruginosa: a molecular epidemiological study.

OBJECTIVES: Here we report on a long-term outbreak from 2009 to 2012 with an XDR Pseudomonas aeruginosa on two wards at a university hospital in southern Germany. METHODS: Whole-genome sequencing was performed on the outbreak isolates and a core genome was constructed for molecular epidemiological analysis. We applied a time-place-sequence algorithm to improve estimation of transmission probabilities. RESULTS: By using conventional infection control methods we identified 49 P. aeruginosa strains, including eight environmental isolates that belonged to ST308 (by MLST) and carried the metallo-ß-lactamase IMP-8. Phylogenetic analysis on the basis of a non-recombinant core genome that contained 22 outbreak-specific SNPs revealed a pattern of four dominant clades with a strong phylogeographic structure and allowed us to determine the potential temporal origin of the outbreak to July 2008, 1 year before the index case was diagnosed. Superspreaders at the root of clades exhibited a high number of probable and predicted transmissions, indicating their exceptional position in the outbreak. CONCLUSIONS: Our results suggest that the initial expansion of dominant sublineages was driven by a few superspreaders, while environmental contamination seemed to sustain the outbreak for a long period despite regular environmental control measures.

Subclone-specific microenvironmental impact and drug response in refractory multiple myeloma revealed by single-cell transcriptomics.

Virtually all patients with multiple myeloma become unresponsive to treatment over time. Relapsed/refractory multiple myeloma (RRMM) is accompanied by the clonal evolution of myeloma cells with heterogeneous genomic aberrations and profound changes of the bone marrow microenvironment (BME). However, the molecular mechanisms that drive drug resistance remain elusive. Here, we analyze the heterogeneous tumor cell population and its complex interaction network with the BME of 20 RRMM patients by single cell RNA-sequencing before/after treatment. Subclones with chromosome 1q-gain express a specific transcriptomic signature and frequently expand during treatment. Furthermore, RRMM cells shape an immune suppressive BME by upregulation of inflammatory cytokines and close interaction with the myeloid compartment. It is characterized by the accumulation of PD1+ γδ T-cells and tumor-associated macrophages as well as the depletion of hematopoietic progenitors. Thus, our study resolves transcriptional features of subclones in RRMM and mechanisms of microenvironmental reprogramming with implications for clinical decision-making.

Signatures of positive selection reveal a universal role of chromatin modifiers as cancer driver genes.

Tumors are composed of an evolving population of cells subjected to tissue-specific selection, which fuels tumor heterogeneity and ultimately complicates cancer driver gene identification. Here, we integrate cancer cell fraction, population recurrence, and functional impact of somatic mutations as signatures of selection into a Bayesian model for driver prediction. We demonstrate that our model, cDriver, outperforms competing methods when analyzing solid tumors, hematological malignancies, and pan-cancer datasets. Applying cDriver to exome sequencing data of 21 cancer types from 6,870 individuals revealed 98 unreported tumor type-driver gene connections. These novel connections are highly enriched for chromatin-modifying proteins, hinting at a universal role of chromatin regulation in cancer etiology. Although infrequently mutated as single genes, we show that chromatin modifiers are altered in a large fraction of cancer patients. In summary, we demonstrate that integration of evolutionary signatures is key for identifying mutational driver genes, thereby facilitating the discovery of novel therapeutic targets for cancer treatment.