Quantifying single nucleotide variant detection sensitivity in exome sequencing

BACKGROUND: The targeted capture and sequencing of genomic regions has rapidly demonstrated its utility in genetic studies. Inherent in this technology is considerable heterogeneity of target coverage and this is expected to systematically impact our sensitivity to detect genuine polymorphisms. To fully interpret the polymorphisms identified in a genetic study it is often essential to both detect polymorphisms and to understand where and with what probability real polymorphisms may have been missed. RESULTS: Using down-sampling of 30 deeply sequenced exomes and a set of gold-standard single nucleotide variant (SNV) genotype calls for each sample, we developed an empirical model relating the read depth at a polymorphic site to the probability of calling the correct genotype at that site. We find that measured sensitivity in SNV detection is substantially worse than that predicted from the naive expectation of sampling from a binomial. This calibrated model allows us to produce single nucleotide resolution SNV sensitivity estimates which can be merged to give summary sensitivity measures for any arbitrary partition of the target sequences (nucleotide, exon, gene, pathway, exome). These metrics are directly comparable between platforms and can be combined between samples to give “power estimates” for an entire study. We estimate a local read depth of 13X is required to detect the alleles and genotype of a heterozygous SNV 95% of the time, but only 3X for a homozygous SNV. At a mean on-target read depth of 20X, commonly used for rare disease exome sequencing studies, we predict 5–15% of heterozygous and 1–4% of homozygous SNVs in the targeted regions will be missed. CONCLUSIONS: Non-reference alleles in the heterozygote state have a high chance of being missed when commonly applied read coverage thresholds are used despite the widely held assumption that there is good polymorphism detection at these coverage levels. Such alleles are likely to be of functional importance in population based studies of rare diseases, somatic mutations in cancer and explaining the “missing heritability” of quantitative traits

Clinical exome performance for reporting secondary genetic findings.

BACKGROUND : Reporting clinically actionable incidental genetic findings in the course of clinical exome testing is recommended by the American College of Medical Genet- ics and Genomics (ACMG). However, the performance of clinical exome methods for reporting small subsets of genes has not been previously reported. METHODS : In this study, 57 exome data sets performed as clinical (n ! 12) or research (n ! 45) tests were retrospec- tively analyzed. Exome sequencing data was examined for adequacy in the detection of potentially pathogenic variant locations in the 56 genes described in the ACMG incidental findings recommendation. All exons of the 56 genes were examined for adequacy of sequencing coverage. In addition, nucleotide positions annotated in HGMD (Human Gene Mutation Database) were examined. RESULTS : The 56 ACMG genes have 18336 nucleotide variants annotated in HGMD. None of the 57 exome data sets possessed a HGMD variant. The clinical exome test had inadequate coverage for " 50% of HGMD vari- ant locations in 7 genes. Six exons from 6 different genes had consistent failure across all 3 test methods; these exons had high GC content (76%–84%). CONCLUSIONS : The use of clinical exome sequencing for the interpretation and reporting of subsets of genes requires recognition of the substantial possibility of inadequate depth and breadth of sequencing coverage at clinically relevant locations. Inadequate depth of coverage may contribute to false-negative clinical ex- ome results

Statistical assessment of genomic variability in tumours and bacterial communities

Current high-throughput DNA sequencing technologies have the ability to generate large amounts of high-resolution genomic data. The high dimensionality in combination with the substantial levels of technical errors and biological variability typically present in the data make, however, the analysis challenging. Tailored statistical methods are therefore crucial for reaching valid biological conclusions. In this thesis, such methods were developed and applied to address research questions in biology and medicine.First, a method for identification of tumour-specific (somatic) mutations was developed, which included steps for noise-reduction, sensitive detection of\ua0 DNA alterations and removal of systematic errors. In Paper I, the method was applied to exome-sequenced paired normal–tumour samples from pheochromocytoma patients. A significantly higher mutation rate was found in malignant compared to benign tumours and three genes with recurrent somatic mutations, exclusively located in malignant tumours, were identified. In paper II and III, somatic mutations were identified in patients with acute myeloid leukemia and evaluated as biomarkers in personalised deep sequencing analysis of remaining cancer cells after treatment. In paper III, a statistical model correcting for position-specific errors in the data was developed and shown to provide superior sensitivity compared to standard techniques. In paper IV, clinically relevant molecular subgroups of metastatic small intestinal neuroendocrine tumours were identified based on miRNA gene expression profiles. Survival analysis and subsequent validation suggested miR-375 as a prognostic biomarker. In paper V, a hierarchical Bayesian model for detecting differences on nucleotide level between microbial communities is proposed. By including between-sample variability and utilizing a shrinkage approach, the model was able to perform well both in cases of few samples and high biological variability. Finally, the model was used to detect antibiotic resistance mutations in bacteria.This thesis demonstrates that dedicated statistical analysis and knowledge of the underlying error structure present in high-dimensional biological data is of importance for enabling accurate interpretation and sound conclusions

A Path to Implement Precision Child Health Cardiovascular Medicine.

Congenital heart defects (CHDs) affect approximately 1% of live births and are a major source of childhood morbidity and mortality even in countries with advanced healthcare systems. Along with phenotypic heterogeneity, the underlying etiology of CHDs is multifactorial, involving genetic, epigenetic, and/or environmental contributors. Clear dissection of the underlying mechanism is a powerful step to establish individualized therapies. However, the majority of CHDs are yet to be clearly diagnosed for the underlying genetic and environmental factors, and even less with effective therapies. Although the survival rate for CHDs is steadily improving, there is still a significant unmet need for refining diagnostic precision and establishing targeted therapies to optimize life quality and to minimize future complications. In particular, proper identification of disease associated genetic variants in humans has been challenging, and this greatly impedes our ability to delineate gene-environment interactions that contribute to the pathogenesis of CHDs. Implementing a systematic multileveled approach can establish a continuum from phenotypic characterization in the clinic to molecular dissection using combined next-generation sequencing platforms and validation studies in suitable models at the bench. Key elements necessary to advance the field are: first, proper delineation of the phenotypic spectrum of CHDs; second, defining the molecular genotype/phenotype by combining whole-exome sequencing and transcriptome analysis; third, integration of phenotypic, genotypic, and molecular datasets to identify molecular network contributing to CHDs; fourth, generation of relevant disease models and multileveled experimental investigations. In order to achieve all these goals, access to high-quality biological specimens from well-defined patient cohorts is a crucial step. Therefore, establishing a CHD BioCore is an essential infrastructure and a critical step on the path toward precision child health cardiovascular medicine

RNA2DNAlign: nucleotide resolution allele asymmetries through quantitative assessment of RNA and DNA paired sequencing data.

We introduce RNA2DNAlign, a computational framework for quantitative assessment of allele counts across paired RNA and DNA sequencing datasets. RNA2DNAlign is based on quantitation of the relative abundance of variant and reference read counts, followed by binomial tests for genotype and allelic status at SNV positions between compatible sequences. RNA2DNAlign detects positions with differential allele distribution, suggesting asymmetries due to regulatory/structural events. Based on the type of asymmetry, RNA2DNAlign outlines positions likely to be implicated in RNA editing, allele-specific expression or loss, somatic mutagenesis or loss-of-heterozygosity (the first three also in a tumor-specific setting). We applied RNA2DNAlign on 360 matching normal and tumor exomes and transcriptomes from 90 breast cancer patients from TCGA. Under high-confidence settings, RNA2DNAlign identified 2038 distinct SNV sites associated with one of the aforementioned asymetries, the majority of which have not been linked to functionality before. The performance assessment shows very high specificity and sensitivity, due to the corroboration of signals across multiple matching datasets. RNA2DNAlign is freely available from http://github.com/HorvathLab/NGS as a self-contained binary package for 64-bit Linux systems

Achieving high-sensitivity for clinical applications using augmented exome sequencing

daSNVs in ACMG genes where inadequate coverage was observed among at least one platform, using WES/ACE data normalized to both 12 Gb and 100× mean coverage. (XLSX 312 kb

The South Asian genome

Genetics of disease Microarrays Variant genotypes Population genetics Sequence alignment AllelesThe genetic sequence variation of people from the Indian subcontinent who comprise one-quarter of the world's population, is not well described. We carried out whole genome sequencing of 168 South Asians, along with whole-exome sequencing of 147 South Asians to provide deeper characterisation of coding regions. We identify 12,962,155 autosomal sequence variants, including 2,946,861 new SNPs and 312,738 novel indels. This catalogue of SNPs and indels amongst South Asians provides the first comprehensive map of genetic variation in this major human population, and reveals evidence for selective pressures on genes involved in skin biology, metabolism, infection and immunity. Our results will accelerate the search for the genetic variants underlying susceptibility to disorders such as type-2 diabetes and cardiovascular disease which are highly prevalent amongst South Asians.Whole genome sequencing to discover genetic variants underlying type-2 diabetes, coronary heart disease and related phenotypes amongst Indian Asians. Imperial College Healthcare NHS Trust cBRC 2011-13 (JS Kooner [PI], JC Chambers)

Statistical Methods for Improving Low Frequency Variant Calling in Cancer Genomics

Cancer is not a single disease, but a family of genomic diseases characterized by a set of initiating genomic variants accumulated in a single cell that allows that cell to begin dividing uncontrollably. Tumors grow by cell division, and each cell division generates a new set of variants that are passed along to its offspring. As a result, at the time of diagnosis a typical tumor of approximately 100,000,000 cells contains hundreds of millions of genomic variants, whose frequency in the population is a function of the time that they arose. Mutation accumulation through both inheritance and de novo variant production results in a final tumor in which the vast majority of variants are present at low frequency. Current methods used to identify variants have difficulty identifying low frequency variants. Here I will describe two algorithms aimed at improving low frequency variant calling in two settings. Patient-Derived Xenografts (PDXs) serve as avatars for individual patient disease as well as invaluable models for studying basic cancer biology. Molecular character- ization of PDXs is common, but the extensive homology between human and mouse genes present special challenges in sequencing tumors grown in mice. In Chapter 2 I describe an algorithm and R implementation called MAPEX that allows labs study- ing PDXs to use commercial sequencing technologies and locally filter false positive variants caused by sequence homology. Detecting somatic mutations within tumors is key to understanding treatment re- sistance, patient prognosis, and tumor evolution. In Chapter 3 I present BATCAVE (Bayesian Analysis Tools for Context-Aware Variant Evaluation), which extends cur- rent state-of-the-art statistical models for tumor variant calling. I also present an R implementation of the algorithm, and show using simulations that the BATCAVE algorithm improves variant detection