Dataset Information

Exome-based Variant Detection in Core Promoters.

ABSTRACT: Core promoter controls the initiation of transcription. Core promoter sequence change can disrupt transcriptional regulation, lead to impairment of gene expression and ultimately diseases. Therefore, comprehensive characterization of core promoters is essential to understand normal and abnormal gene expression in biomedical studies. Here we report the development of EVDC (Exome-based Variant Detection in Core promoters) method for genome-scale analysis of core-promoter sequence variation. This method is based on the fact that exome sequences contain the sequences not only from coding exons but also from non-coding region including core promoters generated by random fragmentation in exome sequencing process. Using exome data from three cell types of CD4+ T cells, CD19+ B cells and neutrophils of a single individual, we characterized the features of core promoter-mapped exome sequences, and analysed core-promoter variation in this individual genome. We also compared the core promoters between YRI (Yoruba in Ibadan, Nigeria) and the CEU (Utah residents of European decedent) populations using the exome data generated by the 1000 Genome project, and observed much higher variation in YRI population than in CEU population. Our study demonstrates that the EVDC method provides a simple but powerful means for genome-wile de novo characterization of core promoter sequence variation.

SUBMITTER: Kim YC

PROVIDER: S-EPMC4964598 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Exome-based Variant Detection in Core Promoters.

Kim Yeong C YC Cui Jian J Luo Jiangtao J Xiao Fengxia F Downs Bradley B Wang San Ming SM

Scientific reports 20160728

Core promoter controls the initiation of transcription. Core promoter sequence change can disrupt transcriptional regulation, lead to impairment of gene expression and ultimately diseases. Therefore, comprehensive characterization of core promoters is essential to understand normal and abnormal gene expression in biomedical studies. Here we report the development of EVDC (Exome-based Variant Detection in Core promoters) method for genome-scale analysis of core-promoter sequence variation. This m ...[more]

PMID: 27464681

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Project description:BackgroundThe 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.ResultsUsing 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.ConclusionsNon-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.

Project description:Rare genetic variation in the human population is a major source of pathophysiological variability and has been implicated in a host of complex phenotypes and diseases. Finding disease-related genes harboring disparate functional rare variants requires sequencing of many individuals across many genomic regions and comparing against unaffected cohorts. However, despite persistent declines in sequencing costs, population-based rare variant detection across large genomic target regions remains cost prohibitive for most investigators. In addition, DNA samples are often precious and hybridization methods typically require large amounts of input DNA. Pooled sample DNA sequencing is a cost and time-efficient strategy for surveying populations of individuals for rare variants. We set out to 1) create a scalable, multiplexing method for custom capture with or without individual DNA indexing that was amenable to low amounts of input DNA and 2) expand the functionality of the SPLINTER algorithm for calling substitutions, insertions and deletions across either candidate genes or the entire exome by integrating the variant calling algorithm with the dynamic programming aligner, Novoalign.We report methodology for pooled hybridization capture with pre-enrichment, indexed multiplexing of up to 48 individuals or non-indexed pooled sequencing of up to 92 individuals with as little as 70 ng of DNA per person. Modified solid phase reversible immobilization bead purification strategies enable no sample transfers from sonication in 96-well plates through adapter ligation, resulting in 50% less library preparation reagent consumption. Custom Y-shaped adapters containing novel 7 base pair index sequences with a Hamming distance of ?2 were directly ligated onto fragmented source DNA eliminating the need for PCR to incorporate indexes, and was followed by a custom blocking strategy using a single oligonucleotide regardless of index sequence. These results were obtained aligning raw reads against the entire genome using Novoalign followed by variant calling of non-indexed pools using SPLINTER or SAMtools for indexed samples. With these pipelines, we find sensitivity and specificity of 99.4% and 99.7% for pooled exome sequencing. Sensitivity, and to a lesser degree specificity, proved to be a function of coverage. For rare variants (?2% minor allele frequency), we achieved sensitivity and specificity of ?94.9% and ?99.99% for custom capture of 2.5 Mb in multiplexed libraries of 22-48 individuals with only ?5-fold coverage/chromosome, but these parameters improved to ?98.7 and 100% with 20-fold coverage/chromosome.This highly scalable methodology enables accurate rare variant detection, with or without individual DNA sample indexing, while reducing the amount of required source DNA and total costs through less hybridization reagent consumption, multi-sample sonication in a standard PCR plate, multiplexed pre-enrichment pooling with a single hybridization and lesser sequencing coverage required to obtain high sensitivity.

Project description:BackgroundLess than two percent of the human genome is protein coding, yet that small fraction harbours the majority of known disease causing mutations. Despite rapidly falling whole genome sequencing (WGS) costs, much research and increasingly the clinical use of sequence data is likely to remain focused on the protein coding exome. We set out to quantify and understand how WGS compares with the targeted capture and sequencing of the exome (exome-seq), for the specific purpose of identifying single nucleotide polymorphisms (SNPs) in exome targeted regions.ResultsWe have compared polymorphism detection sensitivity and systematic biases using a set of tissue samples that have been subject to both deep exome and whole genome sequencing. The scoring of detection sensitivity was based on sequence down sampling and reference to a set of gold-standard SNP calls for each sample. Despite evidence of incremental improvements in exome capture technology over time, whole genome sequencing has greater uniformity of sequence read coverage and reduced biases in the detection of non-reference alleles than exome-seq. Exome-seq achieves 95% SNP detection sensitivity at a mean on-target depth of 40 reads, whereas WGS only requires a mean of 14 reads. Known disease causing mutations are not biased towards easy or hard to sequence areas of the genome for either exome-seq or WGS.ConclusionsFrom an economic perspective, WGS is at parity with exome-seq for variant detection in the targeted coding regions. WGS offers benefits in uniformity of read coverage and more balanced allele ratio calls, both of which can in most cases be offset by deeper exome-seq, with the caveat that some exome-seq targets will never achieve sufficient mapped read depth for variant detection due to technical difficulties or probe failures. As WGS is intrinsically richer data that can provide insight into polymorphisms outside coding regions and reveal genomic rearrangements, it is likely to progressively replace exome-seq for many applications.

Dataset Information

Exome-based Variant Detection in Core Promoters.

Publications

Exome-based Variant Detection in Core Promoters.

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