Low-, high-coverage, and two-stage DNA sequencing in the design of the genetic association study

The ideal reporting of RAS testing in colorectal adenocarcinoma: a pathologists' perspective

RAS gene mutational status represents an imperative predictive biomarker to be tested in the clinical management of metastatic colorectal adenocarcinoma. Even if it is one of the most studied biomarkers in the era of precision medicine, several pre-analytical and analytical factors may still impasse an adequate reporting of RAS status in clinical practice, with significant therapeutic consequences. Thus, pathologists should be aware on the main topics related to this molecular evaluation: (i) adopt diagnostic limit of detections adequate to avoid the interference of sub-clonal cancer cell populations; (ii) choose the most adequate diagnostic strategy according to the available sample and its qualification for molecular testing; (iii) provide all the information regarding the mutation detected, since many RAS mutation-specific targeted therapeutic approaches are in development and will enter into routine clinical practice. In this review, we give a comprehensive description of the current scenario about RAS gene mutational testing in the clinic focusing on the pathologist's role in patient selection for targeted therapies.

CRISPR Genome Editing Technology and its Application in Genetic Diseases: A Review

Gene therapy has been a long lasting goal for scientists, and there are many optimal methods and tools to correct disease-causing mutations in humans. Recently, the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) technology has been progressively adopted for the assessment a treatment of human diseases, including thalassemia, Parkinson's disease, cystic fibrosis, glaucoma, Huntington's disease, and Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome (HIV/AIDS). CRISPR sequences belong to the bacterial immune system, which includes the nuclease Cas enzyme and an RNA sequence. The RNA sequence is unique and pathogen-specific, and identifies and binds to the DNA of invasive viruses, allowing the nuclease Cas enzyme to cut the identified DNA and destroy the invasive viruses. This feature provides the possibility to edit mutations in the DNA sequence of live cells by replacing a specific targeted RNA sequence with the RNA sequence in the CRISPR system. Previous studies have reported the improvement steps in confrontation with human diseases caused by single-nucleotide mutations using this system. In this review, we first introduce CRISPR and its functions and then elaborate on the use of CRISPR in the treatment of human diseases.

Medium-coverage DNA sequencing in the design of the genetic association study

DNA sequencing is a widely used tool in genetic association study. Sequencing cost remains a major concern in sequencing-based study, although the application of next generation sequencing has dramatically decreased the sequencing cost and increased the efficiency. The choice of sequencing depth and the sequencing sample size will largely determine the final study investment and performance. Many studies have been conducted to find a cost-effective design of sequencing depth that can achieve certain sequencing accuracy using minimal sequencing cost. The strategies previously studied can be classified into two groups: (1) single-stage to sequence all the samples using either high (>~30×) or low (<~10×) sequencing depth; and (2) two-stage to sequence an affordable number of individuals at a high-coverage followed by a large sample of low-coverage sequencing. However, limited studies examined the performance of the medium-coverage (10-30×) sequencing depth for a genetic association study, where the optimum sequencing depth may exist. In this study, using a published simulation framework, we comprehensively compared the medium-coverage sequencing (MCS) to the single- and two-stage high/low-coverage sequencing in terms of the power and type I error of the variant discovery and association testing. We found, given certain sequencing effort, MCS yielded a comparable discovery power and better type I error control compared with the best (highest power) scenarios using other high- and low-coverage single-stage or two-stage designs. However, MCS was not as competent as other designs with respect to the association power, especially for the rare variants and when the sequencing investment was limited.

Genomics and Transcriptomics: The Powerful Technologies in Precision Medicine

In a clinical trial, people with the same disease can show different responses after treatment with the same drug and exactly under the same conditions. Some of them may improve, some may not show any response, and occasionally side effects may be observed. In other words, people with the same disease process under the same therapeutic conditions may have different responses. Today, some diseases are resistant to conventional (standard) treatment procedures. Why do people with the same disease show different responses to the treatment with the same drug? This is primarily due to differences in molecular pathways (especially genetic variations) associated with the disease. On the other hand, designing and delivery of a new drug is a time-consuming and costly process, so any mistake in any stage of this process can have irreparable consequences for pharmaceutical companies and consumer patients. Therefore, we can achieve more accurate and reliable treatments by acquiring precise insight into different aspects of precision medicine including genomics and transcriptomics. The aim of this paper is to address the role of genomics and transcriptomics in precision medicine.

Characterization of centromeric satellite DNAs (MALREP) in the Asian swamp eel (Monopterus albus) suggests the possible origin of repeats from transposable elements

Centromeric satellite DNA (cen-satDNA) sequences of the Asian swamp eel (Monopterus albus) were characterized. Three GC-rich cen-satDNA sequences were detected as a 233 bp MALREP-A and a 293 bp MALREP-B localized to all chromosomes, and a 293 bp MALREP-C distributed on eight chromosome pairs. Sequence lengths of MALREP-B and MALREP-C were 60 bp larger than that of MALREP-A, showing partial homology with core sequences (233 bp). Size differences between MALREP-A and MALREP-B/C suggest the possible occurrence of two satDNA families. The presence of an additional 60 bp in MALREP-B/C resulted from an ancient dimer of 233 bp monomers and subsequent mutation and homogenization between the two monomers. All MALREPs showed partial homology with transposable elements (TEs), suggesting that the MALREPs originated from the TEs. The MALREPs might have been acquired in the Asian swamp eel, thereby promoting fixation in the species.

Combining sequence data from multiple studies: Impact of analysis strategies on rare variant calling and association results

Individual sequencing studies often have limited sample sizes and so limited power to detect trait associations with rare variants. A common strategy is to aggregate data from multiple studies. For studying rare variants, jointly calling all samples together is the gold standard strategy but can be difficult to implement due to privacy restrictions and computational burden. Here, we compare joint calling to the alternative of single-study calling in terms of variant detection sensitivity and genotype accuracy as a function of sequencing coverage and assess their impact on downstream association analysis. To do so, we analyze deep-coverage (~82×) exome and low-coverage (~5×) genome sequence data on 2,250 individuals from the Genetics of Type 2 Diabetes study jointly and separately within five geographic cohorts. For rare single nucleotide variants (SNVs): (a) ≥97% of discovered SNVs are found by both calling strategies; (b) nonreference concordance with a set of highly accurate genotypes is ≥99% for both calling strategies; (c) meta-analysis has similar power to joint analysis in deep-coverage sequence data but can be less powerful in low-coverage sequence data. Given similar data processing and quality control steps, we recommend single-study calling as a viable alternative to joint calling for analyzing SNVs of all minor allele frequency in deep-coverage data.

Optimal sequencing depth design for whole genome re-sequencing in pigs

BACKGROUND

As whole-genome sequencing is becoming a routine technique, it is important to identify a cost-effective depth of sequencing for such studies. However, the relationship between sequencing depth and biological results from the aspects of whole-genome coverage, variant discovery power and the quality of variants is unclear, especially in pigs. We sequenced the genomes of three Yorkshire boars at an approximately 20X depth on the Illumina HiSeq X Ten platform and downloaded whole-genome sequencing data for three Duroc and three Landrace pigs with an approximately 20X depth for each individual. Then, we downsampled the deep genome data by extracting twelve different proportions of 0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 and 0.9 paired reads from the original bam files to mimic the sequence data of the same individuals at sequencing depths of 1.09X, 2.18X, 3.26X, 4.35X, 6.53X, 8.70X, 10.88X, 13.05X, 15.22X, 17.40X, 19.57X and 21.75X to evaluate the influence of genome coverage, the variant discovery rate and genotyping accuracy as a function of sequencing depth. In addition, SNP chip data for Yorkshire pigs were used as a validation for the comparison of single-sample calling and multisample calling algorithms.

RESULTS

Our results indicated that 10X is an ideal practical depth for achieving plateau coverage and discovering accurate variants, which achieved greater than 99% genome coverage. The number of false-positive variants was increased dramatically at a depth of less than 4X, which covered 95% of the whole genome. In addition, the comparison of multi- and single-sample calling showed that multisample calling was more sensitive than single-sample calling, especially at lower depths. The number of variants discovered under multisample calling was 13-fold and 2-fold higher than that under single-sample calling at 1X and 22X, respectively. A large difference was observed when the depth was less than 4.38X. However, more false-positive variants were detected under multisample calling.

CONCLUSIONS

Our research will inform important study design decisions regarding whole-genome sequencing depth. Our results will be helpful for choosing the appropriate depth to achieve the same power for studies performed under limited budgets.

Assessment of low-coverage nanopore long read sequencing for SNP genotyping in doubled haploid canola (Brassica napus L.)

Despite the high accuracy of short read sequencing (SRS), there are still issues with attaining accurate single nucleotide polymorphism (SNP) genotypes at low sequencing coverage and in highly duplicated genomes due to misalignment. Long read sequencing (LRS) systems, including the Oxford Nanopore Technologies (ONT) minION, have become popular options for de novo genome assembly and structural variant characterisation. The current high error rate often requires substantial post-sequencing correction and would appear to prevent the adoption of this system for SNP genotyping, but nanopore sequencing errors are largely random. Using low coverage ONT minION sequencing for genotyping of pre-validated SNP loci was examined in 9 canola doubled haploids. The minION genotypes were compared to the Illumina sequences to determine the extent and nature of genotype discrepancies between the two systems. The significant increase in read length improved alignment to the genome and the absence of classical SRS biases results in a more even representation of the genome. Sequencing errors are present, primarily in the form of heterozygous genotypes, which can be removed in completely homozygous backgrounds but requires more advanced bioinformatics in heterozygous genomes. Developments in this technology are promising for routine genotyping in the future.

Impact of index hopping and bias towards the reference allele on accuracy of genotype calls from low-coverage sequencing

BACKGROUND

Inherent sources of error and bias that affect the quality of sequence data include index hopping and bias towards the reference allele. The impact of these artefacts is likely greater for low-coverage data than for high-coverage data because low-coverage data has scant information and many standard tools for processing sequence data were designed for high-coverage data. With the proliferation of cost-effective low-coverage sequencing, there is a need to understand the impact of these errors and bias on resulting genotype calls from low-coverage sequencing.

RESULTS

We used a dataset of 26 pigs sequenced both at 2× with multiplexing and at 30× without multiplexing to show that index hopping and bias towards the reference allele due to alignment had little impact on genotype calls. However, pruning of alternative haplotypes supported by a number of reads below a predefined threshold, which is a default and desired step of some variant callers for removing potential sequencing errors in high-coverage data, introduced an unexpected bias towards the reference allele when applied to low-coverage sequence data. This bias reduced best-guess genotype concordance of low-coverage sequence data by 19.0 absolute percentage points.

CONCLUSIONS

We propose a simple pipeline to correct the preferential bias towards the reference allele that can occur during variant discovery and we recommend that users of low-coverage sequence data be wary of unexpected biases that may be produced by bioinformatic tools that were designed for high-coverage sequence data.

Evaluation and Recommendations for Routine Genotyping Using Skim Whole Genome Re-sequencing in Canola

Whole genome sequencing offers genome wide, unbiased markers, and inexpensive library preparation. With the cost of sequencing decreasing rapidly, many plant genomes of modest size are amenable to skim whole genome resequencing (skim WGR). The use of skim WGR in diverse sample sets without the use of imputation was evaluated in silico in 149 canola samples representative of global diversity. Fastq files with an average of 10x coverage of the reference genome were used to generate skim samples representing 0.25x, 0.5x, 1x, 2x, 3x, 4x, and 5x sequencing coverage. Applying a pre-defined list of SNPs versus de novo SNP discovery was evaluated. As skim WGR is expected to result in some degree of insufficient allele sampling, all skim coverage levels were filtered at a range of minimum read depths from a relaxed minimum read depth of 2 to a stringent read depth of 5, resulting in 28 list-based SNP sets. As a broad recommendation, genotyping pre-defined SNPs between 1x and 2x coverage with relatively stringent depth filtering is appropriate for a diverse sample set of canola due to a balance between marker number, sufficient accuracy, and sequencing cost, but depends on the intended application. This was experimentally examined in two sample sets with different genetic backgrounds: 1x coverage of 1,590 individuals from 84 Australian spring type four-parent crosses aimed at maximizing diversity as well as one commercial F1 hybrid, and 2x coverage of 379 doubled haploids (DHs) derived from a subset of the four-parent crosses. To determine optimal coverage in a simpler genetic background, the DH sample sequence coverage was further down sampled in silico. The flexible and cost-effective nature of the protocol makes it highly applicable across a range of species and purposes.

A method for allocating low-coverage sequencing resources by targeting haplotypes rather than individuals

Background

This paper describes a heuristic method for allocating low-coverage sequencing resources by targeting haplotypes rather than individuals. Low-coverage sequencing assembles high-coverage sequence information for every individual by accumulating data from the genome segments that they share with many other individuals into consensus haplotypes. Deriving the consensus haplotypes accurately is critical for achieving a high phasing and imputation accuracy. In order to enable accurate phasing and imputation of sequence information for the whole population, we allocate the available sequencing resources among individuals with existing phased genomic data by targeting the sequencing coverage of their haplotypes.

Results

Our method, called AlphaSeqOpt, prioritizes haplotypes using a score function that is based on the frequency of the haplotypes in the sequencing set relative to the target coverage. AlphaSeqOpt has two steps: (1) selection of an initial set of individuals by iteratively choosing the individuals that have the maximum score conditional on the current set, and (2) refinement of the set through several rounds of exchanges of individuals. AlphaSeqOpt is very effective for distributing a fixed amount of sequencing resources evenly across haplotypes, which results in a reduction of the proportion of haplotypes that are sequenced below the target coverage. AlphaSeqOpt can provide a greater proportion of haplotypes sequenced at the target coverage by sequencing less individuals, as compared with other methods that use a score function based on haplotype frequencies in the population. A refinement of the initially selected set can provide a larger more diverse set with more unique individuals, which is beneficial in the context of low-coverage sequencing. We extend the method with an approach for filtering rare haplotypes based on their flanking haplotypes, so that only those that are likely to derive from a recombination event are targeted.

Conclusions

We present a method for allocating sequencing resources so that a greater proportion of haplotypes are sequenced at a coverage that is sufficiently high for population-based imputation with low-coverage sequencing. The haplotype score function, the refinement step, and the new approach for filtering rare haplotypes make AlphaSeqOpt more effective for that purpose than previously reported methods for reducing sequencing redundancy.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-017-0353-y) contains supplementary material, which is available to authorized users.

Personalized or Precision Medicine? The Example of Cystic Fibrosis

The advent of the knowledge on human genetics, by the identification of disease-associated variants, culminated in the understanding of human variability. With the genetic knowledge, the specificity of the clinical phenotype and the drug response of each individual were understood. Using the cystic fibrosis (CF) as an example, the new terms that emerged such as personalized medicine and precision medicine can be characterized. The genetic knowledge in CF is broad and the presence of a monogenic disease caused by mutations in the CFTR gene enables the phenotype–genotype association studies (including the response to drugs), considering the wide clinical and laboratory spectrum dependent on the mutual action of genotype, environment, and lifestyle. Regarding the CF disease, personalized medicine is the treatment directed at the symptoms, and this treatment is adjusted depending on the patient’s phenotype. However, more recently, the term precision medicine began to be widely used, although its correct application and understanding are still vague and poorly characterized. In precision medicine, we understand the individual as a response to the interrelation between environment, lifestyle, and genetic factors, which enabled the advent of new therapeutic models, such as conventional drugs adjustment by individual patient dosage and drug type and response, development of new drugs (read through, broker, enhancer, stabilizer, and amplifier compounds), genome editing by homologous recombination, zinc finger nucleases, TALEN (transcription activator-like effector nuclease), CRISPR-Cas9 (clustered regularly interspaced short palindromic repeats-CRISPR-associated endonuclease 9), and gene therapy. Thus, we introduced the terms personalized medicine and precision medicine based on the CF.